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ABT: a comparative analytical survey on Analysis of Breast Thermograms

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Abstract

One of the common causes of death for women worldwide is breast cancer which various screening and imaging techniques can reduce its mortality and treatment costs. Although mammography is the most common imaging modality for screening breast cancer, but it is expensive and uses radiation. As an emerging and promising technique, thermography offers numerous advantages, including the absence of radiation exposure, no discomfort, no invasiveness, and low cost. By recording and analyzing the temperature distribution in breast region, this type of thermography is able to detect abnormalities. Despite these advantages, thermography images may not be interpreted as easy as images obtained from other imaging modalities. This limitation has caused considerable increase in attention towards computer-aided diagnosis in parallel with applying artificial intelligence paradigm in order to interpretation of breast thermograms. Due to the breadth and diversity of these methods from various aspects, a framework is necessary for a better understanding, classification, and evaluation of these methods. In this article, we discuss two important aspects of breast thermogram processing including segmentation of Region of Interest and detection of abnormality. Additionally, to evaluate the existing methods in the two areas, advantages and disadvantages for each have been discussed and compared. The existing methods in two aspects can be divided into two groups, traditional and deep learning-based schemes, which the latter have become increasingly popular. Based on this, it can be said that a framework that is useful for any researcher who needs to gain an overview of breast thermograms and categorize problems and solutions is presented in this study.

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References

  1. Shaikh K, Krishnan S, Thanki R (2021) Artificial Intelligence in Breast Cancer Early Detection and Diagnosis. Springer, Cham

    Book  Google Scholar 

  2. [online]: Available from: https://www.cancer.gov

  3. Ekici S, Jawzal H (2020) Breast cancer diagnosis using thermography and convolutional neural networks. Medical Hypotheses 137:109542

    Article  Google Scholar 

  4. Mashekova A, Zhao Y, Ng EYK, Zarikas V, Fok SC, Mukhmetov O (2022) Early detection of the breast cancer using infrared technology—A comprehensive review. Thermal Sci Eng Progress 27:101142

    Article  Google Scholar 

  5. Rosalidar R, Rahman A, Muharar R, Syahputra M, Arnia F, Syukri M, Pradhan B, Munadi K (2020) A review on recent progress in thermal imaging and deep learning approaches for breast cancer detection. IEEE Access 8:116176–116194

    Article  Google Scholar 

  6. Zuluaga-Gomez J, Zerhouni N, Al Masry Z, Devalland C, Varnier C (2019) A survey of breast cancer screening techniques: Thermography and electrical impedance tomography. J Med Eng Technol 43:305–322

    Article  Google Scholar 

  7. Al Husaini MAS, Habaebi MH, Hameed SA, Islam MR, Gunawan TS (2020) A systematic review of breast cancer detection using thermography and neural networks. IEEE Access 8:208922–208937

    Article  Google Scholar 

  8. Gonzalez-Hernandez JL, Recinella AN, Kandlikar SG, Dabydeen D, Medeiros L, Phatak P (2019) Technology, application and potential of dynamic breast thermography for the detection of breast. Int J Heat Mass Transf 131:558–573

    Article  Google Scholar 

  9. Singh D, Singh AK (2020) Role of image thermography in early breast cancer detection- past, present and future. Comput Methods Programs Biomed 183:105074

    Article  Google Scholar 

  10. Houssein EH, Emam MM, Ali AA, Suganthan PN (2020) Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review. Expert Syst Appl 167:114161

    Article  Google Scholar 

  11. Tariq M, Iqbal S, Ayesha H, Abbas I, Ahmad KT, Niazi MFK (2020) Medical image based breast cancer diagnosis: State of the art and future directions. Expert Syst Appl 167:114095

    Article  Google Scholar 

  12. Wu H, Huo Y, Pan Y, Xu Z, Huang R, Xie Y, Han C, Liu Z, Wang Y (2022) Learning Pre- and Post-contrast Representation for Breast Cancer Segmentation in DCE-MRI. In: Proceedings of the 2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS). pp 355–359

    Google Scholar 

  13. Shah SM, Khan RA, Arif S, Sajid U (2021) Artificial intelligence for breast cancer detection: trends & directions, arXiv preprint

  14. Labrada A, Barkana BD (2022) Breast cancer diagnosis from histopathology images using supervised algorithms. In: Proceedings of the IEEE Symposium on Computer-Based Medical Systems, Shenzen, China. pp 102–110

    Google Scholar 

  15. Zováthi BH, Mohácsi R, Szász AM (2022) Cserey G (2022) Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network. Diagnostics 12:2161

    Article  Google Scholar 

  16. Murtaza G, Shuib L, Wahab AWA, Mujtaba G, Mujtaba G, Nweke HF, Al-garadi MA, Zulfiqar F, Raza G, Azmi NA (2020) Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges. Artif Intell Rev 53(3):1655–1720

    Article  Google Scholar 

  17. Khan AA, Arora AS (2021) Thermography as an economical alternative modality to mammography for early detection of breast cancer. J Healthcare Eng 202:8

    Google Scholar 

  18. Santana M, Pereira J, Monica D, Silva F, Lima N, Sousa F, Arruda G, Lima R, Azevedo W, Dos Santos W (2018) Breast cancer diagnosis based on mammary thermography and extreme learning machines. Res Biomed Eng 34(1):45–53

    Article  Google Scholar 

  19. Sánchez-Cauce R, Pérez-Martín J, Luque M (2021) Multi-input convolutional neural network for breast cancer detection using thermal images and clinical data. Comput Methods Programs Biomed 204:106045

    Article  Google Scholar 

  20. Rezaei Z (2021) A review on image-based approaches for breast cancer detection, segmentation, and classification. Expert Syst Appl 182:115204

    Article  Google Scholar 

  21. Kandlikar S, Perez-Raya I, Raghupathi PG, Hernandez JL, Dabydeen D, Medeiros L, Phatak P (2017) Infrared imaging technology for breast cancer detection – Current status, protocols and new directions. Int J Heat Mass Transf 108:2303–2320

    Article  Google Scholar 

  22. Jiang LJ, Ng EYK, Yeo ACB, Wu S, Pan F, Yau WY et al (2005) A perspective on medical infrared imaging. J Med Eng Technol 29:257–267

    Article  Google Scholar 

  23. Ng EYK (2009) A review of thermography as promising non-invasive detection modality for breast tumor. Int J Therm Sci 48:849–859

    Article  Google Scholar 

  24. Villa E, Arteaga-Marrero N, Ruiz-Alzola J (2020) Performance assessment of low-cost thermal cameras for medical applications. Sensors 20(5):1321

    Article  Google Scholar 

  25. Zhou Y, Herman C (2018) Optimization of skin cooling by computational modeling for early thermographic detection of breast cancer. Int J Heat Mass Transf 126:864–876

    Article  Google Scholar 

  26. Ghafarpour A et al (2016) A review of the dedicated studies to breast cancer diagnosis by thermal imaging in the fields of medical and artificial intelligence sciences. Biomed Res 27(2):543–552

    Google Scholar 

  27. Orchartt TB, Conci A, Lima RCF, Resmini R, Sanchez A (2013) Breast thermography from an image processing viewpoint: a survey. Signal Processing 93:2785–2803

    Article  Google Scholar 

  28. Moghbel M, Mashohor S (2013) A review of computer assisted detection/diagnosis (CAD) in breast thermography for breast cancer detection. Artif Intell Rev 39(4):305–313

    Article  Google Scholar 

  29. Hakim A, Awale RN (2020) Thermal imaging—an emerging modality for breast cancer detection: a comprehensive review. J Med Syst 44:136

    Article  Google Scholar 

  30. Qi H, Diakides NA (2009) Thermal infrared imaging in early breast cancer detection. Augmented Vision Perception in Infrared. Springer, London, U.K., pp 139–152

    Chapter  Google Scholar 

  31. Qi H, Diakides NA (2003) Thermal infrared imaging in early breast cancer detection—a survey of recent research. In: 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

    Google Scholar 

  32. Raghavendra U, Gudigar A, Rao TN, Ciaccio EJ, Ng EYK, Acharya UR (2019) Computer aided diagnosis for the identification of breast cancer using thermogram images: A comprehensive review. Infrared Phys Technol 102:103041

    Article  Google Scholar 

  33. Ibrahim A, Mohammed S, Ali HA (2018) Breast cancer detection and classification using thermography: A review. In: International conference on advanced machine learning technologies and applications. Springer, Cham

    Google Scholar 

  34. Hassan Abed A, Shaaban EM (2021) Modeling Deep Neural Networks For Breast Cancer Thermography Classification: A Review Study. Int J Adv Networking App 13(2):4939–4946

    Google Scholar 

  35. Lahiri B, Bagavathiappan S, Jayakumar T, Philip J (2012) Medical applications of infrared thermography: a review. Thermal Sci Eng Progress 55(4):221–235

    Google Scholar 

  36. Tan J, Ng EYK, Acharya UR, Chee C (2009) Infrared thermography on ocular surface temperature: a review. Infrared Phys Technol 52:97–108

    Article  Google Scholar 

  37. Sathish D, Kamath S, Prasad K, Kadavigere R (2017) Role of normalization of breast thermogram images and automatic classification of breast cancer. Vis Comput 35(1):57–70

    Article  Google Scholar 

  38. Resmini R, Faria da Silva L, Medeiros PR, Araujo AS, Muchaluat-Saade DC, Conci A (2021) A Hybrid Methodology for Breast Screening and Cancer Diagnosis Using Thermography. Comput Biol Med 135:104553

    Article  Google Scholar 

  39. Gogoi UR, Bhowmik MK, Bhattacharjee D, Ghosh AK, Majumdar G (2016) A study and analysis of hybrid intelligent techniques for breast cancer detection using breast thermograms. In: Bhattacharyya S, Dutta P, Chakraborty S (eds) Hybrid soft computing approaches. Springer, New Delhi, pp 329–359

    Chapter  Google Scholar 

  40. Etehad Tavakol M, Ng EYK (2013) Breast thermography as a potential non-contact method in the early detection of cancer: a review. J Mech Med Biol 13(2):1330001

    Article  Google Scholar 

  41. Francis SV, Sasikala M, Bharathi GB, Jaipurkar SD (2014) Breast cancer detection in rotational thermography images using texture features. Infrared Phys Technol 67:490–496

    Article  Google Scholar 

  42. Magalhaes C, Vardasca R, Rebelo M, Valenca-Filipe R, Ribeiro M, Mendes J (2019) Distinguishing melanocytic nevi from melanomas using static and dynamic infrared thermal imaging. J Eur Acad Dermatol Venereol (JEADV) 33:1700–1705

    Article  Google Scholar 

  43. Papež BJ, Palfy M, Mertik M, Turk Z (2009) Infrared thermography based on artificial intelligence as a screening method for carpal tunnel syndrome diagnosis. J Int Med Res 37(3):779–790

    Article  Google Scholar 

  44. Acharya U, Tan J, Koh J, Sudarshan V, Yeo S, Too C, Chua C, Ng E, Tong L (2015) Automated diagnosis of dry eye using infrared thermography images. Infrared Phys Technol 71:263–271

    Article  Google Scholar 

  45. Hernandez-Contreras D, Peregrina-Barreto H, Rangel-Magdaleno J, Ramirez-Cortes J, Renero-Carrillo F (2015) Automatic classification of thermal patterns in diabetic foot based on morphological pattern spectrum. Infrared Phys Technol 73:149–157

    Article  Google Scholar 

  46. Acharya UR, Ng EYK, Tan JH, Sree SV (2012) Thermography based breast cancer detection using texture features and support vector machine. J Med Syst 36(3):1503–1510

    Article  Google Scholar 

  47. Ma J, Shang P, Lu C, Meraghni S, Benaggoune K, Zuluaga J, Zerhouni N, Devalland C, Masry ZA (2019) A portable breast cancer detection system based on smartphone with infrared camera. Vibroeng Procedia 26:57

    Article  Google Scholar 

  48. Al Husaini MAS, Hadi Habaebi M, Gunawan TS, Islam MR (2021) Self-Detection of Early Breast Cancer Application with Infrared Camera and Deep Learning. Electronics 10:2538

    Article  Google Scholar 

  49. Surantha N, Atmaja P, David, Wicaksono M (2021) A review of wearable internet-of-things device for healthcare. Procedia Comput Sci 179:939–943

    Article  Google Scholar 

  50. Fadhillah UDL, Afikah ZAN, Safiee NEN, Asnida AW, Rafiq AKM, Ramlee MH (2018) Development of a low-cost wearable breast cancer detection device. In: 2nd International Conference on Bio Signal Analysis, Processing and Systems (ICBAPS) IEEE. pp 41–46

    Google Scholar 

  51. Singh J, Arora AS (2019) Automated approaches for ROIs extraction in medical thermography: a review and future directions. Multimed Tools App 79:1–24

    Google Scholar 

  52. Oliva D, Hinojosa S, Elaziz MA, Ortega-Sánchez N (2018) Context based image segmentation using antlion optimization and sine cosine algorithm. Multimed Tools App 77(19):25761–25797

    Article  Google Scholar 

  53. Guruprasad, P (2020) Overview of different thresholding methods in image processing. In: TEQIP Sponsored 3rd National Conference on ETACC

  54. Díaz-Cortés MA, Ortega-Sánchez N, Hinojosa S, Oliva D, Cuevas E, Rojas R, Demin A (2018) A multi-level thresholding method for breast thermograms analysis using dragonfly algorithm. Infrared Phys Technol 93:346–361

    Article  Google Scholar 

  55. Houssein EH, Emam MM, Ali AA (2021) An efficient multilevel thresholding segmentation method for thermography breast cancer imaging based on improved chimp optimization algorithm. Expert Syst Appl 185:115651

    Article  Google Scholar 

  56. Rajinikanth V, Couceiro MS (2015) RGB histogram based color image segmentation using firefly algorithm. Procedia Comput Sci 46:1449–1457

    Article  Google Scholar 

  57. Sánchez-Ruiz D, Olmos-Pineda I, Olvera-López JA (2020) Automatic region of interest segmentation for breast thermogram image classification. Pattern Recogn Lett 135:72–81

    Article  Google Scholar 

  58. Krishna S, George B (2021) An affordable solution for the recognition of abnormality in breast thermogram. Multimed Tools App 80(18):28303–28328

    Article  Google Scholar 

  59. Kavya N et al (2021) Asymmetrical Analysis of Breast Thermal Images for Detection of Breast Cancer. In: Mukhopadhyay CK, Mulaveesala R (eds) Advances in Non-destructive Evaluation. Lecture Notes in Mechanical Engineering, Springer, Singapore

    Google Scholar 

  60. Pramanik S, Bhattacharjee D, Nasipuri M (2015) Wavelet based thermogram analysis for breast cancer detection. Int. Symp, Adv. Comput. Commun. (ISACC) 205–21

  61. Fernandes SL, Rajinikanth V, Kadry S (2019) A hybrid framework to evaluate breast abnormality using infrared thermal images. IEEE Consumer Electron Magazine 8:31–36

    Article  Google Scholar 

  62. Raja NSM, Sukanya SA, Nikita Y (2015) Improved PSO based multi-level thresholding for cancer infected breast thermal images using Otsu. Procedia Computer Science 48:524–529

    Article  Google Scholar 

  63. Pare S, Kumar A, Singh GK, Bajaj V (2020) Image segmentation using multilevel thresholding: a research review. Iran J Sci Technol Trans Electr Eng 44(1):1–29

    Article  Google Scholar 

  64. Kumar MJ, Kumar DGVSR, Reddy RVK (2014) Review on image segmentation techniques. Int J Sci Res Eng Technol (IJSRET) 3(6):992–997

    Google Scholar 

  65. Raju PDR, Neelima G (2012) Image Segmentation by using histogram thresholding. Int J Comput Sci Eng Technol 2(1):776–779

    Google Scholar 

  66. Sridevi M, Mala C (2012) A survey on monochrome image segmentation methods. Procedia Technol 6:548–555

    Article  Google Scholar 

  67. Mahmoudi L, Zaart AE (2012) A survey of entropy image thresholding techniques. In: 2012 2nd International conference on advances in computational tools for engineering applications (ACTEA), 204–209

  68. Singla A, Patra S (2016) A fast automatic optimal threshold selection technique for image segmentation. Signal Image Video Process 11:1–8

    Google Scholar 

  69. Patra S, Gautam R, Singla A (2014) A novel context sensitive multilevel thresholding for image segmentation. Appl Soft Comput 23:122–127

    Article  Google Scholar 

  70. Boussaïd I, Julien L, Siarry P (2013) A survey on optimization metaheuristics. Inf Sci 237:82–117

    Article  MathSciNet  MATH  Google Scholar 

  71. Rogowska J (2000) Overview and fundamentals of medical image segmentation. In: Bankman I (ed) Handbook of medical image processing and analysis. Elsevier, Amsterdam, The Netherlands, pp 69–85

    Google Scholar 

  72. Kuruvilla J et al (2016) A review on image processing and image segmentation. In: International conference on data mining and advanced computing (SAPIENCE). IEEE

  73. Ait Lbachir I, Es-salhi R, Daoudi I, Tallal S, Medromi H (2017) A survey on segmentation techniques of mammogram images. Adv Ubiquitous Networking 2:545–556

    Article  Google Scholar 

  74. Mahmoudzadeh E, Montazeri M, Zekri M, Sadri S (2015) Extended hidden markov model for optimized segmentation of breast thermography images. Infrared Phys Technol 72:19–28

    Article  Google Scholar 

  75. Iqbal HT, Majeed B, Khan U, Bin Altaf MA (2019) An infrared high classification accuracy hand-held machine learning based breast-cancer detection system. In: Proc IEEE Biomed Circuits Syst Conf

  76. Moghbel M, Mashohor S, ah Rozi Mahmud H, Saripan MIB (2012) Random walkers based segmentation method for breast thermography. In: IEEE EMBS International Conference on Biomedical Engineering and Sciences Langkawi. pp 627–630

  77. Moghbel M, Mashohor S, Mahmud R, Bin Saripan MI, Hamid SA, Mohamad Sani S, Nizam S (2017) Breast boundary segmentation in thermography images based on random walkers. Turkish J Electr Eng Comput Sci 25:1733–1750

    Article  Google Scholar 

  78. Etehadtavakol M, Emrani Z, Ng EYK (2018) Rapid extraction of the hottest or coldest regions of medical thermographic images. Med Biol Eng Compu 57(2):379–388

    Article  Google Scholar 

  79. Rao BS, Shetty S, Shivaram JM, Umadevi V (2018) Estimation of breast tumour size, location and preprocessing algorithm for the breast thermal signatures. Int J Adv Res, Ideas Innov Technol 4:505–511

    Google Scholar 

  80. Garduño-Ramón MA, Vega-Mancilla SG, Morales-Henández LA, Osornio-Rios RA (2017) Supportive noninvasive tool for the diagnosis of breast cancer using a thermographic camera as sensor. Sensors 17(3):497

    Article  Google Scholar 

  81. Gomathi P, Muniraj C, Periasamy P (2020) Breast thermography based unsupervised anisotropic-feature transformation method for automatic breast cancer detection. Microprocess Microsyst 77:103137

    Article  Google Scholar 

  82. Peng B, Zhang L, Zhang D (2013) A survey of graph theoretical approaches to image segmentation. Pattern Recogn 46(3):1020–1038

    Article  Google Scholar 

  83. Ramesh K, Kumar GK, Swapna K, Datta D, Rajest SS (2021) A review of medical image segmentation algorithms. EAI Endorsed Trans Pervasive Health Technol 7(27):e6

    Google Scholar 

  84. Srivastava A, Lee AB, Simoncelli EP, Zhu SC (2003) On advances in statistical modeling of natural images. J Math Imaging Vision 18(1):17–33

    Article  MathSciNet  MATH  Google Scholar 

  85. Missaoui R, Palenichka RM (2005) Effective image and video mining: An overview of model based approaches. In MDM’05: Proceedings of the 6th International Workshop on Multimedia Data Mining. pp 43–52

  86. Elnakib A, Gimel’farb G, Suri JS, El-Baz A (2011) Medical image segmentation: a brief survey. In: Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies. Springer. pp 1–39

  87. Keshtkar F, Gueaieb W, White A (2005) An agent-based model for image segmentation. In: Proceedings of 13th Multi-disciplinary Iranian Researchers Conference in Europe. Leeds, UK

  88. Norouzi A, Rahim MSM, Altameem A, Saba T, Rada AE, Rehman A, Uddin M (2014) Medical image segmentation methods, algorithms, and applications. IETE Tech Rev 31(3):199–213

    Article  Google Scholar 

  89. Yi F, Moon I (2012) Image segmentation: a survey of graph-cut methods. Int Conf Syst Info (ICSAI) 2012:1936–1941

    Google Scholar 

  90. Nosrati MS, Hamarneh G (2016) Incorporating prior knowledge in medical image segmentation: a survey. arXiv:1607.01092

  91. Ramadan H, Lachqar C, Tairi H (2020) A survey of recent interactive image segmentation methods. Comput Visual Media 6(4):355–384

    Article  Google Scholar 

  92. Lempitsky VS, Kohli P, Rother C, Sharp T (2009) Image segmentation with a bounding box prior. In: Proceedings of the IEEE 12th International Conference on Computer Vision. pp 277–284

  93. Li Y, Sun J, Tang CK, Shum HY (2004) Lazy snapping. ACM Trans Graphics 23(3):303–308

    Article  Google Scholar 

  94. Rother C, Kolmogorov V, Blake A (2004) Grabcut: Interactive foreground extraction using iterated graph cuts. ACM Trans Graphics (TOG) 23:309–314

    Article  Google Scholar 

  95. Roy KK, Phadikar A (2014) Automated medical image segmentation: a survey. In: Proceedings of the international conference on computing, communication & manufacturing

  96. Lak B, Najafi P (2021) Diagnosis of Breast Cancer by Integrating Machine Learning and Machine Vision Techniques in Thermography Images. EasyChair Preprint

  97. Roslidar R, Syaryadhi M, Saddami K, Pradhan B, Arnia F, Syukri M, Munadi K (2021) BreaCNet: a high-accuracy breast thermogram classifier based on mobile convolutional neural network. Math Biosci Eng 19(2):1304–1331

    Article  MATH  Google Scholar 

  98. Zadeh H, Fayazi A, Binazir B, Yargholi M (2021) Breast cancer diagnosis based on feature extraction using dynamic models of thermal imaging and deep autoencoder neural networks. J Test Eval 49:20200044

    Article  Google Scholar 

  99. Morales-Cervantes A, Kolosovas-Machuca ES, Guevara E, Reducindo MM, Hernández ABB, García MR, González FJ (2018) An automated method for the evaluation of breast cancer using infrared thermography. Excli Journal 17:989–998

    Google Scholar 

  100. Lashkari AE, Pak F, Firouzmand M (2016) Full Intelligent Cancer Classification of Thermal Breast Images to Assist Physician in Clinical Diagnostic Applications. J Med Signals & Sensors 6:12–24

    Article  Google Scholar 

  101. Dey S, Roychoudhury R, Malakar S, Sarkar R (2022) Screening of breast cancer from thermogram images by edge detection aided deep transfer learning model. Multimed Tools App 81:1–19

    Article  Google Scholar 

  102. Amya DR, Anandhamala GS (2019) Analysis of Breast Thermograms Using Asymmetry in Infra-Mammary Curves. J Med Syst 43:146

    Article  Google Scholar 

  103. Sathish D, Kamath S, Prasad K, Kadavigere R, Martis RJ (2017) Asymmetry analysis of breast thermograms using automated segmentation and texture features. SIViP 11(4):745–752

    Article  Google Scholar 

  104. Etehad Tavakol M, Chandran V, Ng EYK, Kafieh R (2013) Breast cancer detection from thermal images using bispectral invariant features. Int J Therm Sci 69:21–36

    Article  Google Scholar 

  105. Hossam A, Harb HM, AbdElKader HM (2018) Automatic image segmentation method for breast cancer analysis using thermography. J Eng Sci 46(1):12–32

    Google Scholar 

  106. Sedong M, Jiyoung H, Youngsun K, Yunyoung N, Preap L, Bong-Keun J, Dongik O, Wonhan S (2017) Thermal infrared image analysis for breast cancer detection. KSII Trans Internet Inf Syst 11(2):1134–1147

    Google Scholar 

  107. Yadav P, Jethani V (2016) Breast thermograms analysis for cancer detection using feature extraction and data mining technique. In: Proceedings of the international conference on advances in information communication technology & computing. pp 1–5

    Google Scholar 

  108. Rajinikanth V, Raja NSM, Satapathy SC, Dey N, Devadhas GG (2018) Thermogram assisted detection and analysis of ductal carcinoma in situ (DCIS). In: International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). IEEE. pp 1641–1646

  109. Tello-Mijares S, Woo F, Flores F (2019) Breast Cancer Identification via Thermography Image Segmentation with a Gradient Vector Flow and a Convolutional Neural Network. J Healthcare Eng 2019:13

    Article  Google Scholar 

  110. Ng EYK, Chen Y (2006) Segmentation of breast thermogram: Improved boundary detection with modified snake algorithm. J Mech Med Biol 6:123–136

    Article  Google Scholar 

  111. Madhavi V, Thomas CB (2019) Multi-view breast thermogram analysis by fusing texture features. Quant InfraRed Thermography J 16(1):111–128

    Article  Google Scholar 

  112. Josephine Selle J, Shenbagavalli A, Sriraam N, Venkatraman B, Jayashree M, Menaka M (2018) Automated recognition of ROIs for breast thermograms of lateral view-a pilot study. Quant InfraRed Thermography J 15(2):194–213

    Google Scholar 

  113. Suganthi S, Ramakrishnan S (2014) Anisotropic diffusion filter based edge enhancement for segmentation of breast thermogram using level sets. Biomed Signal Process Control 10(Supplement C):128–136

    Article  Google Scholar 

  114. Vijaya Madhavi, T. Christy Bobby (2017) Thermal Imaging Based Breast Cancer Analysis Using BEMD and Uniform RLBP. 3rd International Conference on Biosignals, images and instrumentation (ICBSII)

  115. Coady J, O'Riordan A, Dooly G, Newe T, Toal D (2019) An overview of popular digital image processing filtering operations. In: 2019 13th international conference on sensing technology (ICST), Sydney, Australia. pp 1–5

  116. Owotogbe J, Ibiyemi T, Adu B (2019) Edge detection techniques on digital images-a review. Int J Innov Sci Res Technol 4:329–332

    Google Scholar 

  117. Singh S, Datar A (2013) EDGE detection techniques using Hough transform. Int J Emerging Technol Adv Eng 3(6):333–337

    Google Scholar 

  118. Dhankhar P, Sahu N (2013) A review and research of edge detection techniques for image segmentation. Int J Comput Sci Mob Comput 2(7):86–92

    Google Scholar 

  119. Gong X-Y, Su H, Xu D, Zhang Z-T, Shen F, Yang H-B (2018) An overview of contour detection approaches. Int J Autom Comput 15(6):656–672

    Article  Google Scholar 

  120. He L, Peng Z, Everding B et al (2008) A comparative study of deformable contour methods on medical image segmentation. Image Vis Comput 26(2):141–163

    Article  Google Scholar 

  121. Jayadevappa D, SrinivasKumar S, Murty DS (2011) Medical image segmentation algorithms using deformable models: a review. IETE Tech Rev 28(3):248–255

    Article  Google Scholar 

  122. Baswaraj D, Govardhan A, Premchand P (2012) Active contours and image segmentation: the current state of the art. Glob J Comput Sci Technol. 12(11):1–12

    Google Scholar 

  123. Jiang X, Zhang R, Nie S (2012) Image segmentation based on level set method. Phys Procedia 33:840–845

    Article  Google Scholar 

  124. Baral B, Gonnade S, Verma T (2014) Image segmentation and various segmentation techniques—A review. Int J Soft Comput Eng 4:2231–2307

    Google Scholar 

  125. Mittal H, Pandey AC, Saraswat M, Kumar S, Pal R, Modwel G (2021) A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets. Multimed Tools App 81:35001

    Article  Google Scholar 

  126. Thilagamani S, Shanthi N (2011) A survey on image segmentation through clustering. Int J Res Rev Inf Sci 1(1):14–17

    Google Scholar 

  127. Hankare P, Shah K, Nair D et al (2016) Breast cancer detection using thermography. Int Res J Eng Technol (IRJET) 4(3):2395–2356

    Google Scholar 

  128. Francis SV, Sasikala M, Saranya S (2014) Detection of breast abnormality from thermograms using curvelet transform based feature extraction. J Med Syst 38(4):1–9

    Article  Google Scholar 

  129. Shahari S, Wakankar A (2015) Color analysis of thermograms for breast cancer detection. In: 2015 international conference on industrial instrumentation and control (ICIC). IEEE. pp 1577–1581

  130. Zarei M, Rezai A, Hamidpour S.S.F (2021) Breast cancer segmentation based on modified Gaussian mean shift algorithm for infrared thermal images. Comput. Methods Biomech. Biomed. Eng: Imag. Visual.

  131. Dinsha D, Manikandaprabu N (2014) Breast tumor segmentation and classification using SVM and Bayesian from thermogram images. Unique J Unique J Eng Adv Sci 2(2):147–151

    Google Scholar 

  132. Kaur D, Kayr Y (2014) Various image sementation techniques: a review. Int J Comput Sci Mob Comput 3(5):414–809

    Google Scholar 

  133. Wan F, Deng F (2011) Remote sensing image segmentation using mean shift method. In: Lin, S., Huang, X. (Eds.), Advanced Research on Computer Education, Simulation and Modeling. Springer, Berlin Heidelberg. pp 86-90

  134. Dhanachandra N, Chanu YJ (2017) A survey on image segmentation methods using clustering techniques. Eur J Eng Res Sci 2(1):15

    Article  Google Scholar 

  135. Liu X, Song L, Liu S, Zhang Y (2021) A review of deep-learning-based medical image segmentation methods. Sustainability 13(3):1224

    Article  Google Scholar 

  136. Kakileti ST, Dalmia A, Manjunath G (2019) Exploring deep learning networks for tumour segmentation in infrared images. Quant InfraRed Thermography J 17(3):153–168

    Article  Google Scholar 

  137. Kanimozhi P, Sathiya S, Balasubranian M, Sivaguru P, Sivaraj P (2021) Evaluation of machine learning algorithms and deep learning approaches to classify breast cancer using thermography. Psychol Educ 58:8796–8813

    Google Scholar 

  138. Mohamed EA, Rashed EA, Gaber T, Karam O (2022) Deep learning model for fully automated breast cancer detection system from thermograms. PLoS ONE 17(1):e0262349

    Article  Google Scholar 

  139. Kanimozhi P, Sathiya S, Balasubramanian M, Sivaraj P (2021) Novel segmentation method to diagnose breast cancer in thermography using deep convolutional neural network. Annals of R.S.C.B. 25

  140. Cardenas CE, Yang J, Anderson BM, Court LE, Brock KB (2019) Advances in Auto-Segmentation. Semin Radiat Oncol 29(3):185–197

    Article  Google Scholar 

  141. Barbosa VAF, Santana MA, Andrade MKS, Lima RCF, Santos WP (2020) Deep-wavelet neural networks for breast cancer early diagnosis using mammary termographies. In: Das H, Pradhan C, Dey N (eds) Deep Learning for Data Analytics: Foundations, Biomedical Applications, and Challenges, 1st edn. Academic Press, London

    Google Scholar 

  142. Araújo MC, Lima RC, De Souza RM (2014) Interval symbolic feature extraction for thermography breast cancer detection. Expert Syst Appl 41(15):6728–6737

    Article  Google Scholar 

  143. Lennox N, Haskins B (2020) Contrasting classifiers for the detection of breast cancer using thermographic images. Proceedings of the 2nd International Conference on Intelligent and Innovative Computing. pp 1–9

  144. Sharma R, Sharma J.B, Maheshwari R (2021) Comparative analysis of different texture features in breast abnormality prediction. 2nd International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS 2021)

  145. Mishra V, Rath SK (2021) Detection of breast cancer tumours based on feature reduction and classification of thermograms. Quant InfraRed Thermography J 18(5):300–313

    Article  Google Scholar 

  146. Pramanik S, Banik D, Bhattacharjee D, et al (2019) A computer-aided hybrid framework for early diagnosis of breast cancer. Advanced computing and systems for security. Singapore: Springer. pp 111–124

  147. Abdel-Nasser M, Moreno A, Puig D (2019) Breast cancer detection in thermal infrared images using representation learning and texture analysis methods. Electronics 8(1):100

    Article  Google Scholar 

  148. Gupta KK, Vijay R, Pahadiya P, Saxena S (2022) Use of novel thermography features of extraction and different artificial neural network algorithms in breast cancer screening. Wireless Pers Commun 123:1–30

    Article  Google Scholar 

  149. Rajinikanth V, Kadry S, Taniar D, Damaševičius R, Rauf HT (2021) Breast-cancer detection using thermal images with marine-predators-algorithm selected features. In: 2021 seventh international conference on bio signals, images, and instrumentation (ICBSII). IEEE, pp 1–6

    Google Scholar 

  150. Silva LF, Santos AAS, Bravo RS, Silva AC, Muchaluat-Saade DC, Conci A (2016) Hybrid analysis for indicating patients with breast cancer using temperature time series. Comput Methods Programs Biomed 130:142–153

    Article  Google Scholar 

  151. Zhang D, Lu G (2004) Review of shape representation and description techniques. Pattern Recogn 37(1):1–19

    Article  Google Scholar 

  152. Santana MA, Pereira JMS, Lima RCF, Santos WP (2020) Breast lesions classification in frontal thermographic images using intelligent systems and moments of haralick and zernike. In: dos Santos IWP, de Santana MA, da Silva WWA (eds) Understanding a Cancer Diagnosis, 1st edn. Nova Science, New York, pp 65–80

    Google Scholar 

  153. Hakim A, Awale RN (2021) Identification of breast abnormality from thermograms based on fractal geometry features. In: 5th International Conference on Information and Communication Technology for Intelligent Systems (ICTIS-2021). India. pp 23–24

  154. Nasrudin MV, Yaakob NS, Abdul Rahim NA, Zahir Ahmad MZ, Ramli N, Aziz Rashid MS (2021) Moment Invariants Technique for Image Analysis and Its Applications: A Review. J Phys: Conf Ser 1962(1):012028

    Google Scholar 

  155. Humeau-Heurtier A (2019) Texture feature extraction methods: A survey. IEEE Access 7:8975–9000

    Article  Google Scholar 

  156. Gaber T, Ismail G, Anter A, Soliman M, Ali M, Semary N, Hassanien A.E, Snasel V (2015) Thermogram breast cancer prediction approach based on neutrosophic sets and fuzzy c-means algorithm. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). pp 4254–4257

  157. Roslida, Muchamad MK, Arnia F, Syukri M, Munadi K (2021) A conceptual framework of deploying a trained cnn model for mobile breast self-screening, in 2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology. pp 533–537

  158. Kumar G, Bhatia PK (2014) A detailed review of feature extraction in image processing systems. In: 2014 fourth international conference on advanced computing and communication technologies, IEEE. pp 5–12

  159. Silva TAEd, Silva LFd, Muchaluat-Saade DC, Conci A (2020) A computational method to assist the diagnosis of breast disease using dynamic thermography. Sensors 20(14):3866

    Article  Google Scholar 

  160. Araújo MC, Souza RMCR, Lima RCF, Silva Filho TM (2017) An interval prototype classifier based on a parameterized distance applied to breast thermographic images. Med Biol Eng Compu 55:873–884

    Article  Google Scholar 

  161. Lahiri B, Bagavathiappan S, Jayakumar T, Philip J (2012) Medical applications of infrared thermography: a review. Infrared Phys Technol 55(4):221–235

    Article  Google Scholar 

  162. Al Rasyid MB, Yunidar, Arnia F, Munadi K (2018) Histogram Statistics and GLCM Features of Breast Thermograms for Early Cancer Detection. In: 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-NCON2018). pp 120- 124

  163. Al-Antari MA, Kim T-S (2020) Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms. Comput Methods Programs Biomed 196:105584

    Article  Google Scholar 

  164. Nirkhede A, Adkine R, Lohit B, Lande D, Nitnaware S (2020) Classification of Thermography Breast Images for Cancer Detection using Machine Learning. Int J Sci Res Sci, Eng Technol 7:1–6

    Google Scholar 

  165. Zuluaga GJ, Al Masry Z, Benaggoune K, Meraghni S, Zerhouni N (2019) A CNN-based methodology for breast cancer diagnosis using thermal images. Comput Methods Biomech Biomed Eng: Imaging & Visual 9:131–145

    Google Scholar 

  166. Mishra S, Prakash A, Roy SK, Sharan P, Mathur N (2020) Breast cancer detection using thermal images and deep learning. In: proceeding of 7th Int. Conf. Comput for Sustain Global Develop INDIACom

  167. Al Husaini MAS, Habaebi MH, Gunawan TS, Islam MR, Elsheikh EAA, Suliman FM (2022) Thermal-based early breast cancer detection using inception V3, inception V4 and modified inception MV4. Neural Comput Appl 34(1):333–348

    Article  Google Scholar 

  168. Yadav SS, Jadhav SM (2020) Thermal infrared imaging based breast cancer diagnosis using machine learning techniques. Multimed Tools App 81:13139–13157

    Article  Google Scholar 

  169. Fernández-ovies FJ, De Andrés EJ (2019) Detection of breast cancer using infrared thermography and deep neural networks. Springer, Berlin

    Book  Google Scholar 

  170. Goncalves CB, Souza JR, Fernandes H (2021) Classification of static infrared images using pre-trained CNN for breast cancer detection. In: Presented at 2021 34th International Symposium on Computer-Based Medical Systems (CBMS)

  171. Farooq MA, Corcoran P (2020) Infrared imaging for human thermography and breast tumor classification using thermal images. In: 2020 31st Irish Signals and Systems Conference (ISSC). IEEE. pp 1–6

  172. Roslidar R, Saddami K, Arnia F, Syukri M, Munadi K. (2019) A study of fine-tuning CNN models based on thermal imaging for breast cancer classification. In: 2019 IEEE international conference on cybernetics and computational intelligence (CyberneticsCom). pp 77–81

  173. Torres-Galván JC, Guevara E, Kolosovas-Machuca ES, Oceguera-Villanueva A, Flores JL, González FJ (2022) Deep convolutional neural networks for classifying breast cancer using infrared thermography. Quant InfraRed Thermography J 19(4):283–294

    Article  Google Scholar 

  174. Cabıoğlu Ç, Oğul H (2020) Computer-aided breast cancer diagnosis from thermal images using transfer learning. Paper presented at the Bioinformatics and Biomedical Engineering: Proceedings 8th International Work-Conference, IWBBIO 2020. Granada, Spain, pp 716–726

    Chapter  Google Scholar 

  175. Ensafi M, Keyvanpour MR, Shojaedini SV (2022) A New method for promote the performance of deep learning paradigm in diagnosing breast cancer: improving role of fusing multiple views of thermography images. Heal Technol 12:1097–1107

    Article  Google Scholar 

  176. Shojaedini S.V, Firouzmand M, Majidzadeh K, et al (2023) A Framework for Promoting Passive Breast Cancer Monitoring: Deep Learning as an Interpretation Tool for Breast Thermograms. Iran J Med Physics (IJMP)

  177. Fujita H (2020) AI-based computer-aided diagnosis (AI-CAD): the latest review to read first. Radiol Phys Technol 13:6–19

    Article  Google Scholar 

  178. Doupe P, Faghmous J, Basu S (2019) Machine learning for health services researchers. Value in Health 22:808–815

    Article  Google Scholar 

  179. Buhrman H, De Wolf R (2002) Complexity measures and decision tree complexity: a survey. Theoret Comput Sci 288(1):21–43

    Article  MathSciNet  MATH  Google Scholar 

  180. Bossaerts P, Murawski C (2017) Computational complexity and human decision-making. Trends Cogn Sci 21(12):917–929

    Article  Google Scholar 

  181. Sarker IH (2021) Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Computer Science 2(6):1–20

    Article  MathSciNet  Google Scholar 

  182. Keyvanpour MR, Vahidian S, Mirzakhani Z (2021) An analytical review of texture feature extraction approaches. Int J Comput Appl Technol 65(2):118–133

    Article  Google Scholar 

  183. Pouyanfar S, Sadiq S, Yan Y, Tian H, Tao Y, Reyes MP, Shyu ML, Chen SC, Iyengar S (2018) A survey on deep learning: algorithms, techniques, and applications. ACM Computing Surveys (CSUR) 51(5):1–36

    Article  Google Scholar 

  184. Htet ZW, Koldaev VD, Teplova YO, Kremer EA, Fedorov PA (2018) The evaluation of computational complexity of moment invariants in image processing. In: 2018 IEEE Conference of russian young researchers in electrical and electronic engineering (EIConrus). pp 1844–1848

  185. Joo JM (2006) Boundary geometric moments and its application to automatic quality control in the industry. Industrial Data 9(1):76–84

    Google Scholar 

  186. Singh C, Walia E, Pooja S, Upneja R (2012) Analysis of algorithms for fast computation of pseudo Zernike moments and their numerical stability. Digital Signal Processing 22(6):1031–1043

    Article  MathSciNet  Google Scholar 

  187. Madiajagan M, Raj SS (2019) Parallel computing, graphics processing unit (GPU) and new hardware for deep learning in computational intelligence research. Deep learning and parallel computing environment for bioengineering systems. Elsevier, Amsterdam, pp 1–15

    Google Scholar 

  188. Hu X, Chu L, Pei J, Liu W, Bian J (2021) Model complexity of deep learning: a survey. Knowl Inf Syst 63:2585–2619

    Article  Google Scholar 

  189. JrC Traina, Traina AJM, Wu L, Faloutsos C (2010) Fast feature selection using fractal dimension. JIDM 1(1):3–16

    Google Scholar 

  190. Pakhira MK (2014) A linear time-complexity k-means algorithm using cluster shifting. In: 2014 International conference on computational intelligence and communication networks, IEEE. pp 1047–1051

  191. Amayeh G, Tavakkoli A, Bebis G (2009) Accurate and efficient computation of gabor features in real-time applications. In: Advances in Visual Computing. Springer. pp 243–252

  192. Periyasamy S, Prakasarao A, Menaka M, Venkatraman B, Jayashree M (2022) Support Vector Machine based Methodology for Classification of Thermal Images Pertaining to Breast Cancer. J Thermal Biol 110

  193. Singh D, Singh AK, Tiwari S (2022) Early Thermographic Screening of Breast Abnormality in Women with Dense Breast by Thermal, Fractal, and Statistical Analysis. In: Kakileti, S.T., et al. Artificial Intelligence over Infrared Images for Medical Applications and Medical Image Assisted Biomarker Discovery. MIABID AIIIMA 2022 2022. Lecture Notes in Computer Science, vol 13602. Springer, Cham

  194. Pare S, Bhandari AK, Kumar A, Singh GK (2015) Satellite image segmentation based on different objective functions using genetic algorithm: a comparative study. In: 2015 IEEE international conference on digital signal processing (DSP), IEEE

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  1. Department of Computer Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran

    Mahsa Ensafi & Mohammad Reza Keyvanpour

  2. Electrical Engineering Department, Iranian Research Organization for Science and Technology (IROST), Tehran, Iran

    Seyed Vahab Shojaedini

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Ensafi, M., Keyvanpour, M.R. & Shojaedini, S.V. ABT: a comparative analytical survey on Analysis of Breast Thermograms. Multimed Tools Appl (2023). https://doi.org/10.1007/s11042-023-17566-1

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