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A new automated segmentation and classification of mammogram images

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Abstract

Early-stage recognition of lesions is the better probable manner for fighting against breast cancer to find a disease with the highest ratio of malignancy around women. Existing approaches are generally based on deep learning that has been designed for the segmentation of tumors, however, it is complex because of the false positives and the inaccurate detection of boundaries for segmentation, as the existing models incorrectly predict the positive classes, thus affecting the overall classification. In this paper, an enhanced mammogram image classification is proposed by introducing novel segmentation and classification approaches. The initial process of the proposed model is pre-processing, which is performed by the median filtering that tends to remove the noise from the images. The preprocessed images are subjected to segment the tumor from the mammogram images by a new segmentation approach termed Region growing with Adaptive Fuzzy C-Means Clustering (RG-AFCM). Once the segmentation of the tumor is done, feature extraction is performed, where the features are extracted using Gray-Level Run-Length Matrix (GLRM) and Grey Level Co-occurrence Matrix (GLCM) approaches. Furthermore, the extracted features are classified using optimal trained Recurrent Neural Networks (RNN). Here, a new algorithm named Average Fitness New Updating-based Grasshopper Optimization Algorithm (AFU-GOA) is proposed for enhancing both the segmentation and classification phases. Finally, the performance of RG-AFCM-based segmentation is compared over the stat-of-the-art segmentation approaches, and optimal trained RNN is compared over the existing classifiers and deep learning models to prove the reliability of the proposed model. The accuracy of the developed AFU-GOA-RNN is 1%, 2%, 1%, and 3% enhanced than PSO-RNN, GWO-RNN, FF-RNN, and GOA-RNN. Hence, the proposed classification using AFU-GOA-based trained RNN establishes a better performance than existing models.

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References

  1. Adhikari SK, Sing JK, Basu DK, Nasipuri M (2015) Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images. Appl Soft Comput 34:758–769

    Article  Google Scholar 

  2. Ashraf AB, Gavenonis SC, Daye D, Mies C, Rosen MA, Kontos D (2013) A multichannel Markov random field framework for tumor segmentation with an application to classification of gene expression-based breast Cancer recurrence risk. IEEE Trans Med Imaging 32(4):637–648

    Article  Google Scholar 

  3. Been Lim H, Nhung NTT, Li E-P, Duc Thang N (2008) Confocal microwave imaging for breast Cancer detection: delay-multiply-and-sum image reconstruction algorithm. IEEE Trans Biomed Eng 55(6):1697–1704

    Article  Google Scholar 

  4. Bonyadi MR, Michalewicz Z (2016) Analysis of stability, local convergence, and transformation sensitivity of a variant of the particle swarm optimization algorithm. IEEE Trans Evol Comput 20(3):370–385

    Article  Google Scholar 

  5. Chemtex RM, Kantheti S, Kantheti S (2020) Classification of Skin cancer using deep learning, Convolutional Neural Networks -Opportunities and vulnerabilities- A systematic Review. Int J Modern Trends Sci Technol 6(11):101–108

    Article  Google Scholar 

  6. Dheeba J, Albert Singh N, Tamil Selvi S (2014) Computer-aided detection of breast cancer on mammograms: a swarm intelligence optimized wavelet neural network approach. J Biomed Inform 49:45–52

    Article  Google Scholar 

  7. Duraisamy S, Emperumal S (2017) Computer-aided mammogram diagnosis system using deep learning convolutional fully complex-valued relaxation neural network classifier. IET Comput Vis 11(8):656–662

    Article  Google Scholar 

  8. Feng X, Song L, Wang S, Song H, Chen H, Liu Y, Lou C, Zhao J, Liu Q, Liu Y, Zhao R, Xing K, Li S, Yu Y, Liu Z, Yin C, Han B, du Y, Xin R, … Zhou F (2019) Accurate prediction of neoadjuvant chemotherapy pathological complete remission (pCR) for the four sub-types of breast cancer. IEEE Access 7:134697–134706

    Article  Google Scholar 

  9. Gao X, Liao L (2010) A new one-layer neural network for linear and quadratic programming. IEEE Trans Neural Netw 21(6):918–929

    Article  Google Scholar 

  10. Geweid GGN, Abdallah MA (2019) A novel approach for breast Cancer investigation and recognition using M-level set-based optimization functions. IEEE Access 7:136343–136357

    Article  Google Scholar 

  11. GhouseBasha TS, Aloysius G, Rajakumar BR, Giri Prasad MN, Sridevi PV (2012) A constructive smart antenna beam-forming technique with spatial diversity. IET Microwaves, Antennas & Propagation 6(7):773–780

    Article  Google Scholar 

  12. Gong M, Liu J, Li H, Cai Q, Su L (2015) A multiobjective sparse feature learning model for deep neural networks. IEEE Trans Neural Netw Learn Syst 26(12):3263–3277

    Article  MathSciNet  Google Scholar 

  13. Ibrahim A, Mohammed S, Ali HA, Hussein SE (2020) Breast Cancer segmentation from thermal images based on chaotic Salp swarm algorithm. IEEE Access 8:122121–122134

    Article  Google Scholar 

  14. Jameel S (2021) Malebary and Arshad Hashmi, "automated breast mass classification system using deep learning and ensemble learning in digital mammogram,". IEEE Access 9:55312–55328

    Article  Google Scholar 

  15. Kao T, Boverman G, Kim BS, Isaacson D, Saulnier GJ, Newell JC, Choi MH, Moore RH, Kopans DB (2008) Regional Admittivity spectra with Tomosynthesis images for breast Cancer detection: preliminary patient study. IEEE Trans Med Imaging 27(12):1762–1768

    Article  Google Scholar 

  16. Kaura P, Singh G (2019) Parminder Kaur "intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification," informatics in medicine unlocked. Vol. 16:100239

    Google Scholar 

  17. Lee H, Park J, Hwang JY (2020) Channel attention module with multiscale grid average pooling for breast Cancer segmentation in an ultrasound image. IEEE Trans Ultrason Ferroelectr Freq Control 67(7):1344–1353

    Google Scholar 

  18. Li F, Liu M (2019) A hybrid Convolutional and Recurrent Neural Network for Hippocampus Analysis in Alzheimer's Disease. J Neurosci Methods 323:108–118

    Article  Google Scholar 

  19. Mahmood T, Li J, Pei Y, Akhtar F, Imran A, Rehman KU (2020) A brief survey on breast Cancer diagnostic with deep learning schemes using multi-image modalities. IEEE Access 8:165779–165809

    Article  Google Scholar 

  20. Malebary SJ, Hashmi A (2021) Automated breast mass classification system using deep learning and ensemble learning in digital mammogram. IEEE Access 9:55312–55328

    Article  Google Scholar 

  21. Malipatil S, Maheshwari V, Chandra MB (2020) Area Optimization of CMOS Full Adder Design Using 3T XOR. International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), pp 192–194

    Google Scholar 

  22. Michael E, He M, Li H, Kulwa F, Li J (2021) Breast cancer segmentation methods: current status and future potentials. Biomed Res Int

  23. Muduli D, Dash R, Majhi B (2020) Automated breast cancer detection in digital mammograms: a moth flame optimization based ELM approach. Biomedical Signal Processing and Control 59:101912

    Article  Google Scholar 

  24. Mugahed A, Al-antari, 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 Prog Biomed 196:105584

    Article  Google Scholar 

  25. Nirmala Sreedharan NP, Ganesan B, Raveendran R, Sarala P, Dennis B, R. Boothalingam R. (2018) Grey wolf optimisation-based feature selection and classification for facial emotion recognition. IET Biometrics 7(5):490–499

    Article  Google Scholar 

  26. Ong C, Shao S, Yang J (2010) An improved algorithm for the solution of the regularization path of support vector machine. IEEE Trans Neural Netw 21(3):451–462

    Article  Google Scholar 

  27. Pinker K, Perry N, Milner S et al (2010) Accuracy of breast cancer detection with full-field digital mammography and integral computer-aided detection correlated with breast density as assessed by a new automated volumetric breast density measurement system. Breast Cancer Res 12

  28. Pramanik S, Ghosh DB, Nasipuri M (2020) Segmentation of breast-region in breast Thermogram using arc-approximation and triangular-space search. IEEE Trans Instrum Meas 69(7):4785–4795

    Article  Google Scholar 

  29. Quellec G, Lamard M, Bekri L, Cazuguel G, Roux C, Cochener B (2010) Medical case retrieval from a Committee of Decision Trees. IEEE Trans Inf Technol Biomed 14(5):1227–1235

    Article  MATH  Google Scholar 

  30. Radhakrishnan M, Kuttiannan T (2012) Comparative Analysis of Feature Extraction Methods for the Classification of Prostate Cancer from TRUS Medical Images. IJCSI International Journal of Computer Science Issues 9(1)

  31. Rajeshwari S (2020) Patil and Nagashettappa Biradar "automated mammogram breast cancer detection using the optimized combination of convolutional and recurrent neural network,". Evol Intel

  32. Rajeshwari S (2020) Patil and Nagashettappa Biradar, "improved region growing segmentation for breast cancer detection: progression of optimized fuzzy classifier,". International Journal of Intelligent Computing and Cybernetics

    Google Scholar 

  33. Roslidar R, Rahman A, Muharar R, Syahputra MR, 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 

  34. Saha M, Chakraborty C (2018) Her2Net: a deep framework for semantic segmentation and classification of cell membranes and nuclei in breast Cancer evaluation. IEEE Trans Image Process 27(5):2189–2200

    Article  MathSciNet  MATH  Google Scholar 

  35. Wessam M. Salama, Moustafa H. Aly" Deep learning in mammography images segmentation and classification: Automated CNN approach," Alexandria Engineering Journal, Volume 60, Issue 5, Pages 4701–4709, 2021.

  36. Salih AM, Kamil M y (2019) Mammography images segmentation based on fuzzy set and thresholding. Al-Mustansiriyah Journal of Science 29(168)

  37. Saremi S, Mirjalili S (2017) Andrew Lewis "grasshopper optimisation algorithm: theory and application,". Adv Eng Softw 105:30–47

    Article  Google Scholar 

  38. T. Sathya Priya, and Dr. T. Ramaprabha "Resnet based feature extraction with decision tree classifier for Classificaton of mammogram images," Turkish Journal of Computer and Mathematics Education, Vol.12, No.2, pp. 1147–1153, 2021.

  39. Tang J, Rangayyan RM, Xu J, Naqa IE, Yang Y (2009) Computer-aided detection and diagnosis of breast Cancer with mammography: recent advances. IEEE Trans Inf Technol Biomed 13(2):236–251

    Article  Google Scholar 

  40. Vijayarajeswari R, Parthasarathy P, Vivekanandan S, Alavudeen Basha A (2019) Classification of mammogram for early detection of breast cancer using SVM classifier and Hough transform. Measurement 146:800–805

    Article  Google Scholar 

  41. Wang Y, Liu L, Zhang H, Cao Z, Lu S (2010) Image segmentation using active contours with normally biased GVF external force. IEEE Signal Processing Letters 17(10):875–878

    Article  Google Scholar 

  42. Wang Z, Li M, Wang H, Jiang H, Yao Y, Zhang H, Xin J (2019) Breast Cancer detection using extreme learning machine based on feature fusion with CNN deep features. IEEE Access 7:105146–105158

    Article  Google Scholar 

  43. Woten DA, Lusth J, El-Shenawee M (2007) Interpreting artificial neural networks for microwave detection of breast Cancer. IEEE Microwave and Wireless Components Letters 17(12):825–827

    Article  Google Scholar 

  44. Xu J, Xiang L, Liu Q, Gilmore H, Wu J, Tang J, Madabhushi A (2016) Stacked sparse autoencoder (SSAE) for nuclei detection on breast Cancer histopathology images. IEEE Trans Med Imaging 35(1):119–130

    Article  Google Scholar 

  45. Yang X-S (2013) Multiobjective firefly algorithm for continuous optimization. Eng Comput 29(2):175–184

    Article  Google Scholar 

  46. Yap MH, Pons G, Marti J, Ganau S, Sentis M, Zwiggelaar R, Davison AK, Marti R, Moi Hoon Yap, Pons G, Marti J, Ganau S, Sentis M, Zwiggelaar R, Davison AK, Marti R (2018) Automated breast ultrasound lesions detection using convolutional neural networks. IEEE Journal of Biomedical and Health Informatics 22(4):1218–1226

    Article  Google Scholar 

  47. Zheng J, Lin D, Gao Z, Wang S, He M, Fan J (2020) Deep learning assisted efficient AdaBoost algorithm for breast Cancer detection and early diagnosis. IEEE Access 8:96946–96954

    Article  Google Scholar 

  48. Zhu Y, Huang C (2012) An improved median filtering algorithm for image noise reduction. Phys Procedia 25:609–616

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics & Communication, B.L.D.E.A’s V.P.Dr. P.G.H. College of Engg. & Tech (Affiliated to Visvesvaraya Technological University), Vijayapur, India

    Rajeshwari S. Patil

  2. Department of Electronics & Communication Engineering, Bheemanna Khandre Institute of Technology, Bhalki, India

    Nagashettappa Biradar

  3. Department of Electrical and Electronics, B.L.D.E.A’s V. P. Dr. P. G. Halakatti College of Engineering and Technology (Affiliated to Visvesvaraya Technological University), Vijayapur, India

    Rashmi Pawar

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  1. Rajeshwari S. Patil
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Patil, R.S., Biradar, N. & Pawar, R. A new automated segmentation and classification of mammogram images. Multimed Tools Appl 81, 7783–7816 (2022). https://doi.org/10.1007/s11042-022-11932-1

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  • DOI: https://doi.org/10.1007/s11042-022-11932-1

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