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A Study on the Bat Algorithm Technique to Evaluate the Skin Melanoma Images

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Applications of Bat Algorithm and its Variants

Part of the book series: Springer Tracts in Nature-Inspired Computing ((STNIC))

Abstract

Skin melanoma is one of the major cancers in the people with the Caucasian race. Owing to its consequence, a considerable number of research works are proposed by the researchers to develop the probable computer-based assessment technique for the skin melanoma image (SMI). This work aims to develop and implement a computerized tool for the assessment of the SMI based on the recent machine learning technique. In the proposed work, the bat algorithm (BA)-assisted examination technique is implemented to process the SMI. In this work, a detailed evaluation of traditional and the recent version of the BA are considered to assess the performance of the proposed technique. This work considers the variants of BA, such as Levy-Flight (LF), Brownian-Walk (BW) and the Ikeda-Map (IM) to pre-process the skin melanoma pictures. The pre-processed SMIs are then processed with the DRLS segmentation approach and the performance of the considered BAs is validated by computing the essential image performance metrics (IPM), and the result of this study confirms that the final outcome attained with the BW-guided BA offered better result compared to the LF and IM-based techniques. This technique is tested with the PH2 database and the overall IPM attained with the BW-based BA is >93.26%.

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References

  1. https://www.who.int/uv/faq/skincancer/en/index1.html

  2. Glaister J, Wong A, Clausi DA (2014) Segmentation of skin lesions from digital images using joint statistical texture distinctiveness. IEEE Trans Biomed Eng 61(4):1220–1230

    Article  Google Scholar 

  3. Amelard R, Glaister J Wong A, Clausi DA (2015) High-level intuitive features (HLIFs) for intuitive skin lesion description. IEEE Trans Biomed Eng 62(3):820–831

    Google Scholar 

  4. Rajinikanth V, Raja NSM, Arunmozhi S (2019) ABCD rule implementation for the skin melanoma assessment—a study. In: IEEE international conference on system, computation, automation and networking (ICSCAN). IEEE, pp 1–4. https://doi.org/10.1109/icscan.2019.8878860

  5. Rajinikanth V, Satapathy SC, Dey N, Fernandes SL, Manic KS (2019) Skin melanoma assessment using Kapur’s entropy and level set—a study with bat algorithm. Smart Innov Syst Technol 104:193–202. https://doi.org/10.1007/978-981-13-1921-1_19

    Article  Google Scholar 

  6. Dey N, Rajinikanth V, Ashour AS, Tavares JMRS (2018) Social group optimization supported segmentation and evaluation of skin melanoma images. Symmetry 10(2):51. https://doi.org/10.3390/sym10020051

    Article  MATH  Google Scholar 

  7. Amelard R, Glaister J, Wong A, Clausi DA (2013) Melanoma decision support using lighting-corrected intuitive feature models. In: Computer vision techniques for the diagnosis of skin cancer. Series in bioengineering, pp 193–219

    Google Scholar 

  8. Kowsalya N et al (2018) Skin-melanoma evaluation with Tsallis’s thresholding and Chan-Vese approach. In: IEEE international conference on system, computation, automation and networking (ICSCA), pp 1–5. https://doi.org/10.1109/icscan.2018.8541178

  9. Kuwahara H, Furukawa H, Kitamura K et al (2011) Sentinel lymph node detection in melanoma using real-time fluorescence navigation with indocyanine green. Skin Cancer 26:55–58

    Article  Google Scholar 

  10. Niakosari F, Kahn HJ, McCready D et al (2008) Lymphatic invasion identified by monoclonal antibody D2-40, younger age, and ulceration: predictors of sentinel lymph node involvement in primary cutaneous melanoma. Arch Dermatol 144:462–467

    Article  Google Scholar 

  11. Fernandes SL et al (2019) A reliable framework for accurate brain image examination and treatment planning based on early diagnosis support for clinicians. Neural Comput Appl 1–12. https://doi.org/10.1007/s00521-019-04369-5

  12. Hueston JT (1970) lntegumentectomy for malignant melanoma of the limbs. Aust N Z J Surg 40:114–118

    Article  Google Scholar 

  13. Jones RF, Dickinson WE (1972) Total integumentectomy of the leg for multiple in-transit metastases of melanoma. Am J Surg 123:588–590

    Article  Google Scholar 

  14. Mali B, Miklavcic D, Campana LG et al (2013) Tumor size and effectiveness of electrochemotherapy. Radiol Oncol 47:32–41

    Article  Google Scholar 

  15. Spratt DE, Gordon-Spratt EA, Wu S et al (2014) Efficacy of skin-directed therapy for cutaneous metastases from advanced cancer: a meta-analysis. J Clin Oncol: Off J Am Soc Clin Oncol 32:3144–3155

    Article  Google Scholar 

  16. Rubin AI, Chen EH, Ratner DT (2005) Basal-cell carcinoma. N Engl J Med 353:226269

    Article  Google Scholar 

  17. Hayashi T, Furukawa H, Oyama A et al (2012) Dominant lymph drainage in the facial region: evaluation of lymph nodes of facial melanoma patients. Int J Clin Oncol 17:330–335

    Article  Google Scholar 

  18. Nguyen CL, McClay EF, Cole DJ et al (2001) Melanoma thickness and histology predict sentinel lymph node status. Am J Surg 181:8–11

    Article  Google Scholar 

  19. Paek SC, Griffith KA, Johnson TM et al (2007) The impact of factors beyond Breslow depth on predicting sentinel lymph node positivity in melanoma. Cancer 109:100–108

    Article  Google Scholar 

  20. Burmeister BH, Mark Smithers B, Burmeister E et al (2006) A prospective phase II study of adjuvant postoperative radiation therapy following nodal surgery in malignant melanoma—Trans Tasman Radiation Oncology Group (TROG) Study 96.06. Radiother Oncol 81:136–142

    Article  Google Scholar 

  21. Kunz MW, Stolz W (2018) ABCD rule, Dermoscopedia Organization. https://dermoscopedia.org/ABCD_rule. Accessed 17 Jan 2018

  22. Ma Z, Tavares JMRS (2014) Segmentation of skin lesions using level set method. In: Computational modeling of objects presented in images. Fundamentals, methods, and applications. Lecture notes in computer science, vol 8641. Springer, pp 228–233

    Google Scholar 

  23. Dey N et al (2019) Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality. Biocybern Biomed Eng 39(3):843–856. https://doi.org/10.1016/j.bbe.2019.07.005

    Article  Google Scholar 

  24. Pugalenthi R et al (2019) Evaluation and classification of the brain tumor MRI using machine learning technique. Control Eng Appl Inf 21(4):12–21

    Google Scholar 

  25. Satapathy SC, Rajinikanth V (2018) Jaya algorithm guided procedure to segment tumor from brain MRI. J Optim 2018:12. https://doi.org/10.1155/2018/3738049

    Article  MATH  Google Scholar 

  26. He T, Pamela MB, Shi F (2016) Curvature manipulation of the spectrum of a Valence–Arousal-related fMRI dataset using a Gaussian-shaped fast fourier transform and its application to fuzzy KANSEI adjective modeling. Neurocomputing 174:1049–1059

    Article  Google Scholar 

  27. Hore S, Chakroborty S, Ashour AS, Dey N, Ashour AS, Sifakipistolla D, Bhattacharya T, Bhadra Chaudhuri SR (2015) Finding contours of hippocampus brain cell using microscopic image analysis. J Adv Microsc Res 10(2):93–103

    Article  Google Scholar 

  28. Rajinikanth V, Dey N, Kumar R, Panneerselvam J, Raja NSM (2019) Fetal head periphery extraction from ultrasound image using Jaya algorithm and Chan-Vese segmentation. Procedia Comput Sci 152:66–73. https://doi.org/10.1016/j.procs.2019.05.028

    Article  Google Scholar 

  29. Acharya UR et al (2019) Automated detection of Alzheimer’s disease using brain MRI images—a study with various feature extraction techniques. J Med Syst 43(9):302. https://doi.org/10.1007/s10916-019-1428-9

    Article  Google Scholar 

  30. Jahmunah V et al (2019) Automated detection of schizophrenia using nonlinear signal processing methods. Artif Intell Med 100:101698. https://doi.org/10.1016/j.artmed.2019.07.006

    Article  Google Scholar 

  31. Yang XS (2011) Bat algorithm for multi-objective optimization. Int. J. Bio-Inspired Comput 3:267–274

    Article  Google Scholar 

  32. Yang XS (2010) Nature-inspired metaheuristic algorithms, 2nd edn. Luniver Press, United Kingdom

    Google Scholar 

  33. Raja NSM, Rajinikanth V, Fernandes SL, Satapathy SC (2017) Segmentation of breast thermal images using Kapur’s entropy and hidden Markov random field. J Med Imaging Health Inform 7(8):1825–1829

    Article  Google Scholar 

  34. Roopini TI, Vasanthi M, Rajinikanth V, Rekha M, Sangeetha M (2018) Segmentation of tumor from brain MRI using fuzzy entropy and distance regularised level set. Lect Notes Electr Eng 490:297–304. https://doi.org/10.1007/978-981-10-8354-9_27

    Article  Google Scholar 

  35. Rajinikanth V, Fernandes SL, Bhushan B, Sunder NR (2018) Segmentation and analysis of brain tumor using Tsallis entropy and regularised level set. Lect Notes Electr Eng 434:313–321

    Article  Google Scholar 

  36. https://www.fc.up.pt/addi/ph2%20database.html

  37. Jayabarathi T, Raghunathan T, Gandomi AH (2018) The bat algorithm, variants and some practical engineering applications: a review. In: Yang X-S (ed) Nature-inspired algorithms and applied optimization. SCI, vol 744. Springer, Cham, pp 313–330. https://doi.org/10.1007/978-3-319-67669-2_14

  38. Gandomi AH, Yang XS, Alavi AH, Talatahari S (2013) Bat algorithm for constrained optimization tasks. Neural Comput Appl 22(6):1239–1255

    Article  Google Scholar 

  39. Gandomi AH, Yang XS (2014) Chaotic bat algorithm. J Comput Sci 5(2):224–232

    Article  MathSciNet  Google Scholar 

  40. http://vip.uwaterloo.ca/demos/skin-cancer-detection

  41. https://challenge.kitware.com/#challenge/5aab46f156357d5e82b00fe5

  42. Satapathy SC et al (2018) Multi-level image thresholding using Otsu and chaotic bat algorithm. Neural Comput Appl 29(12):1285–1307. https://doi.org/10.1007/s00521-016-2645-5

    Article  Google Scholar 

  43. Li C, Xu C, Gui C, Fox MD (2010) Distance regularized level set evolution and its application to image segmentation. IEEE Trans Image Process 19(12):3243–3254

    Article  MathSciNet  Google Scholar 

  44. Rajinikanth V, Dey N, Satapathy SC, Ashour AS (2018) An approach to examine magnetic resonance angiography based on Tsallis entropy and deformable snake model. Future Gener Comput Syst 85:160–172

    Article  Google Scholar 

  45. Revanth K et al (2018) Computational investigation of stroke lesion segmentation from Flair/DW modality MRI. In: Fourth international conference on biosignals, images and instrumentation (ICBSII). IEEE, pp 206–212. https://doi.org/10.1109/icbsii.2018.8524617

  46. Rajinikanth V, Raja NSM, Kamalanand K (2017) Firefly algorithm assisted segmentation of tumor from brain MRI using Tsallis function and Markov random field. J Control Eng Appl Inform 19(3):97–106

    Google Scholar 

  47. Amin J, Sharif M, Yasmin M et al (2018) Big data analysis for brain tumor detection: deep convolutional neural networks. Future Gener Comput Syst 87:290–297

    Article  Google Scholar 

  48. Fernandes SL, Rajinikanth V, Kadry S (2019) A hybrid framework to evaluate breast abnormality. IEEE Consum Electron Mag 8(5):31–36. https://doi.org/10.1109/MCE.2019.2905488

    Article  Google Scholar 

  49. Dey N, Ashour AS, Bhattacharyya S (2019) Applied nature-inspired computing: algorithms and case studies. Springer tracts in nature-inspired computing

    Google Scholar 

  50. Dey N (ed) (2017) Advancements in applied metaheuristic computing. IGI Global

    Google Scholar 

  51. Dey N (2020) Applications of Firefly algorithm and its variants. Springer tracts in nature-inspired computing

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Information Technology, Techno India College of Technology, Kolkata, 700156, West Bengal, India

    Nilanjan Dey

  2. Department of Electronics and Instrumentation Engineering, St. Josephs College of Engineering, Chennai, 600119, Tamil Nadu, India

    V. Rajinikanth

  3. Department of Computer Science and Engineering Technology, University of Houston-Downtown, Houston, TX, 77002-1001, USA

    Hong Lin

  4. College of Information and Engineering, Wenzhou Medical University, Wenzhou, 325035, China

    Fuqian Shi

Authors
  1. Nilanjan Dey
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  2. V. Rajinikanth
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  3. Hong Lin
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  4. Fuqian Shi
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Corresponding author

Correspondence to V. Rajinikanth .

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Editors and Affiliations

  1. Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, India

    Nilanjan Dey

  2. Department of Electronics and Instrumentation Engineering, St. Josephs College of Engineering, Chennai, Tamil Nadu, India

    V. Rajinikanth

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Dey, N., Rajinikanth, V., Lin, H., Shi, F. (2021). A Study on the Bat Algorithm Technique to Evaluate the Skin Melanoma Images. In: Dey, N., Rajinikanth, V. (eds) Applications of Bat Algorithm and its Variants. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-15-5097-3_3

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