Deep-CNN for Disease Classification using Enhanced Mammographic Images
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Abstract
Purpose: Breast cancer has become one of the most common diseases that women face today as a result of poor nutrition and other environmental factors. A mammogram image of the breast will help detect breast cancer, but still, sometimes doctors and radiologists are unable to detect it due to poor image quality or abnormal region that appears to be normal.
Materials and Methods: In this paper, a deep CNN-based classification model is proposed that classifies the mammogram image as normal, masses, and micro-calcification. Firstly, the PSNR values of the mammogram images is improved using a median filter with the Local Contrast Modification (LCM) method. It is further enhanced by Adaptive-CLAHE in con junction with the Wiener filter. After image enhancement, the region of interest is segmented through morphological feature extraction and the Otsu thresholding method.
Results: In order to increase the number of samples in the mammogram image dataset, image data augmentation is applied to segmented images.
Conclusion: Finally, a pre-trained ResNet model is used for the classification of mammogram images. The proposed model has shown improved PSNR for mammogram images and achieved a higher classification accuracy of 98.91%, thus outperforming other existing methods. Additionally, the explainability and causality of the proposed model are also discussed to show the learning process of the model.
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33- F. Jiang and H. Liu, “Breast Mass Lesion Classification in Mammograms by Transfer Learning”, ICBCB ’17 Hong Kong, no. September (2018).
34- L. Shen, L. R. Margolies, J. H. Rothstein, E. Fluder, R. McBride, and W. Sieh, “Deep Learning to Improve Breast Cancer Detection on Screening Mammography,” Sci. Rep., 9 (1), pp. 1–12, (2019).
35- L. G. Falconi, M. Perez, and W. G. Aguilar, “Transfer Learning in Breast Mammogram Abnormalities Classification with Mobilenet and Nasnet”, Int. Conf. Syst. Signals, Image Process., vol. 2019-June, pp. 109–114, (2019).
36- Lepori, D., “Inflammatory breast disease: The radiologist’s role”, Diagn Interv Imaging, 96: 1045-64, (2015).
37- F. Ma, L. Yu, G. Liu, and Q. Niu, “Computer aided mass detection in mammography with temporal change analysis”, Comput. Sci. Inf. Syst., vol. 12 (4), pp. 1255–1272, (2015).
38- S. H. Kassani, P. H. Kassani, M. J. Wesolowski, K. A. Schneider, and R. Deters, “Breast Cancer Diagnosis with Transfer Learning and Global Pooling”, ICTC 2019- 10th Int. Conf. ICT Converg. ICT Converg. Lead. Auton. Futur., no. c, pp. 519–524, (2019).
39- L. Alzubaidi, O. Al-Shamma, M. A. Fadhel, L. Farhan, J. Zhang, and Y. Duan, “Optimizing the performance of breast cancer classification by employing the same domain transfer learning from hybrid deep convolutional neural network model”, Electron., vol. 9, no. 3, (2020).
40- S. Guan and M. Loew, “Breast cancer detection using transfer learning in convolutional neural networks.,” Proc. Appl. Imag. Pattern Recognit. Work., vol. 2017-October, (2018).
2- Q. Hadi, I. Masroor, and Z. Hussain, “Mammographic Criteria for Determining the Diagnostic Accuracy of Microcalcifications in the Detection of Malignant Breast Lesions”, Cureus, 11 (10), (2019).
3- Jaime R. Herndon, MS, MPH, “Normal and Ab- normal Mammogram Images.”, online available at: https://www.verywellhealth.com/mammogram-images-descriptions-and-details-4020351. (accessed on Sept 30, 2022.)
4- N. Z. Sonthineni, Chaitra, Namita Mohindra, and Agrawal Vinita, “Correlation of digital mammography and digital breast tomosynthesis features of self detected breast cancers with human epidermal growth factor receptor type 2/neu status”, South Asian J. cancer, 7 (2), pp. 171– 174., (2018).
5- Becker, Sven, “A historic and scientific review of breast cancer: The next global healthcare challenge”, International Journal of Gynecology, and Obstetrics. 131: S36–S39, (2015).
6- L. M. B. Hashem, R. H. M. Ali, M. H. Helal, E. El. E. L. Gemeae, and A. F. I. Moustafa, “Characterization of breast masses: a comparative study between automated breast ultrasound (ABUS) and digital breast tomosynthesis (DBT)”, Egypt. J. Radiol. Nucl. Med., 51 (1), (2020).
7- Bland, Kirby I, Edward M Copeland, V Suzanne Klimberg, and William J Gradishar (2017), The Breast E-Book: Comprehensive Management of Benign and Malignant Diseases (Elsevier Health Sciences).
8- Heywang-Kobrunner SH, Hacker A, Sedlacek S, “Advantages and Disadvantages of Mammography Screening”, Breast Care (Basel). 2011;6(3):199-207. doi: 10.1159/000329005. Epub 2011 May 27.
9- S. J. S. Gardezi, A. Elazab, B. Lei, and T. Wang, “Breast cancer detection and diagnosis using mammographic data: Systematic review”, J. Med. Internet Res., 21 (7), pp. 1–22, (2019).
10- Komen, Susan G, “Breast Calcifications”, American Cancer Society. pp. 4–5, (2020).
11- K. He, X. Zhang, S. Ren and d J. Sun, “Deep Residual Learning for Image Recognition”, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, pp. 770 778, (2016).
12- M. T. Ribeiro, S. Singh and C. Guestrin, “Why Should I Trust You? Explaining the Predictions of Any Classifier”, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2016).
13- M. R. Shelda Mohan, “Optimized Histogram Based Contrast Limited Enhancement for Mammogram Images”, ACEEE Int. J. Inf. Technol., 3 (1), pp. 66–71, (2013).
14- K. Kipli et al., “Morphological and Otsu’s thresholding-based retinal blood vessel segmentation for detection of retinopathy”, Int. J. Eng. Technol., 7 (3), pp. 16–20, (2018).
15- N. Alam, E. R. E. Denton, and R. Zwiggelaar, “Classification of micro- calcification clusters in digital mammograms using a stack generalization based classifier”, J. Imaging, 5 (9), (2019).
16- R. A. L. Al-juboori, “Contrast Enhancement of the Mammographic Image Using Retinex with CLAHE methods CLAHE”, Iraqi J. Sci., 58 (1), pp. 327–336, (2017).
17- K. Akila, L. S. Jayashree, and A. Vasuki, “Mammographic image enhancement using indirect contrast enhancement techniques - A com- parative study”, Procedia Comput. Sci., vol. 47, no. C, pp. 255–261, (2015).
18- T. Tot, M. Gere, S. Hofmeyer, A. Bauer, and U. Pellas, “The clinical value of detecting microcalcifications on a mammogram”, Semin. Cancer Biol., no. October, (2019).
19- A. Kamra, V. K. Jain, S. Singh, and S. Mittal, “Characterization of Architectural Distortion in Mammograms Based on Texture Analysis Using Support Vector Machine Classifier with Clinical Evaluation”, J. Digit. Imaging, 29 (1), pp. 104–114, (2016).
20- J. R. Quinlan, “Induction of decision trees”, Mach. Learn., vol. 1, no. 1, pp. 81–106, (1986).
21- Yousef, Waleed A et al., “Method and System for Image Analysis to Detect Cancer”. arXiv 26(19): 1–21, (2019).
22- Zou, L.; Yu, S.; Meng, T.; Zhang, Z.; Liang, X.; Xie, Y., “A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis”, Comput. Math. Methods Med. 2019, vol:2019, pp 1–16, (2019).
23- Guo, R.; Lu, G.; Qin, B.; Fei, B., “Ultrasound Imaging Technologies for Breast Cancer Detection and Management: A Review”. Ultrasound Med. Biol. 2018, 44, 37–70, (2018).
24- Zhu, Wentao, et al., “Deep Multi-Instance Networks with Sparse La- bel Assignment for Whole Mammogram Classification”, CoRR, vol. abs/1612.05968. (2016).
25- I. Sechopoulos and R. M. Mann, “Stand-alone artificial intelligence - The future of breast cancer screening?”, Breast, vol. 49, pp. 254–260, (2020).
26- M. Sundaram, K. Ramar, N. Arumugam, G. Prabin, “Histogram Mod- ified Local Contrast Enhancement for mammogram images”, Applied Soft Computing, 11 (8), pp. 5809-5816, (2011).
27- Jintasuttisak, T.; Intajag, S., “Color Retinex Image Enhancement by Rayleigh Contrast Limited Histogram Equalization”, International Con- ference on Control, Automation and Systems.,10,692-697, (2014).
28- N. Otsu, “A threshold selection method from gray-level histogram”, IEEE Trans. Syst. Man Cybern, vol. 9, no. 1, pp. 62-66, (1997).
29- A. Pal, R. Srivastva, Y. N. Singh, “CardioNet: An Efficient ECG Arrhythmia Classification System Using Transfer Learning”, Big Data Research, Vol. 26, (2021), 100271.
30- Shorten, C., Khoshgoftaar, T.M., “A survey on Image Data Augmenta- tion for Deep Learning”, Journal of Big Data 6 (60), (2019).
31- C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, C. Liu, “Survey on deep transfer learning”, in: 27th International Conference on Artificial Neural Networks, Rhodes, Proceedings, Part III, (2018).
32- Suckling, J.; Parker, J.; Dance, D.; Astley, S.; Hutt, I.; Boggis, C.; Rick- etts, I.; Stamatakis, E.; Cerneaz, N.; Kok, S.; et al. Mammographic Image Analysis Society (MIAS) database v1. 21. Med. Imaging 2014 PACS Imaging Inform. Next Gener. Innov. 2015, (2015), 9039. Available online: https://www.repository.cam.ac.uk/handle/1810/250394/ (accessed on 30 September 2022).
33- F. Jiang and H. Liu, “Breast Mass Lesion Classification in Mammograms by Transfer Learning”, ICBCB ’17 Hong Kong, no. September (2018).
34- L. Shen, L. R. Margolies, J. H. Rothstein, E. Fluder, R. McBride, and W. Sieh, “Deep Learning to Improve Breast Cancer Detection on Screening Mammography,” Sci. Rep., 9 (1), pp. 1–12, (2019).
35- L. G. Falconi, M. Perez, and W. G. Aguilar, “Transfer Learning in Breast Mammogram Abnormalities Classification with Mobilenet and Nasnet”, Int. Conf. Syst. Signals, Image Process., vol. 2019-June, pp. 109–114, (2019).
36- Lepori, D., “Inflammatory breast disease: The radiologist’s role”, Diagn Interv Imaging, 96: 1045-64, (2015).
37- F. Ma, L. Yu, G. Liu, and Q. Niu, “Computer aided mass detection in mammography with temporal change analysis”, Comput. Sci. Inf. Syst., vol. 12 (4), pp. 1255–1272, (2015).
38- S. H. Kassani, P. H. Kassani, M. J. Wesolowski, K. A. Schneider, and R. Deters, “Breast Cancer Diagnosis with Transfer Learning and Global Pooling”, ICTC 2019- 10th Int. Conf. ICT Converg. ICT Converg. Lead. Auton. Futur., no. c, pp. 519–524, (2019).
39- L. Alzubaidi, O. Al-Shamma, M. A. Fadhel, L. Farhan, J. Zhang, and Y. Duan, “Optimizing the performance of breast cancer classification by employing the same domain transfer learning from hybrid deep convolutional neural network model”, Electron., vol. 9, no. 3, (2020).
40- S. Guan and M. Loew, “Breast cancer detection using transfer learning in convolutional neural networks.,” Proc. Appl. Imag. Pattern Recognit. Work., vol. 2017-October, (2018).
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| Issue | Vol 12 No 2 (2025) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v12i2.18287 | |
| Keywords | ||
| Mammogram Image Breast Cancer Micro- calcification Transfer Learning | ||
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How to Cite
1.
Vaish R, Shukla P. Deep-CNN for Disease Classification using Enhanced Mammographic Images. Frontiers Biomed Technol. 2025;12(2):435-445.
