Automatic Classification of Breast Cancer Images Using Transfer Learning on Enhanced Mammography Images
Subject Areas : electrical and computer engineeringZahra Amiri 1 , Zahra Mortezaie 2
1 - Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran
2 - Department of Computer Sciences, Faculty of Mathematics and Computer Sciences, Hakim Sabzevari University, Sabzevar, Iran
Keywords: Computer Vision, Breast Cancer, Deep Learning, Image Enhancement,
Abstract :
Breast cancer is considered one of the major concerns in global health, and it is divided into two types: benign and malignant. The malignant type poses a higher risk due to its faster metastasis. Therefore, there is a critical need for fast and accurate detection. Despite the expertise of radiologists, errors due to incorrect interpretation often lead to misdiagnoses. To address this issue, this paper proposes an intelligent system for analyzing mammography images, which includes preprocessing, feature extraction, and classification stages. In this system, the image quality is first improved using preprocessing techniques like Contrast Limited Adaptive Histogram Equalization (CLAHE), and then the region corresponding to the cancerous mass is extracted using Otsu’s thresholding segmentation method. Additionally, key features for distinguishing between benign and malignant tumors are extracted using two pre-trained Convolutional Neural Network (CNN) models, namely ResNet50 and InceptionV3. Finally, the extracted features are analyzed using a Support Vector Machine (SVM) classifier to predict the tumor types. The result of this work is an improvement in diagnostic accuracy and early breast cancer detection, which reduces human error and the current challenges in interpreting mammography images
[1] S. Łukasiewicz, M. Czeczelewski, A. Forma, J. Baj, R. Sitarz and A. Stanisławek, "Breast Cancer—Epidemiology, Risk Factors, Classification, Prognostic Markers, and Current Treatment Strategies—an updated review.," Cancers, vol. 13, no. 17, p. 4287, 2021.
[2] C. Cruz-Ramos, O. García-Avila, J. Almaraz-Damian, V. Ponomaryov, R. Reyes-Reyes and S. Sadovnychiy, "Benign and Malignant Breast Tumor Classification in Ultrasound and Mammography Images via Fusion of Deep Learning and Handcraft Feature," Entropy, vol. 25, no. 7, p. 991, 2023.
[3] K. Korhonen, S. Weinstein, E. McDonald and E. Conant, "Strategies to Increase Cancer Detection: Review of True-positive and False-negative Results at Digital Breast Tomosynthesis Screening," Radiographics, vol. 36, no. 7, pp. 1954-1965, 2016.
[4] E. Ekpo, M. Alakhras and P. Brennan, "Errors in Mammography Cannot be sSolved Through Technology Alone," Asian Pacific journal of cancer prevention, vol. 19, no. 2, p. 291, 2018.
[5] L. Nicosia, G. Gnocchi, I. Gorini, M. Venturini, F. Fontana, F. Pesapane, I. Abiuso, A. Bozzini, M. Pizzamiglio, A. Latronico and F. Abbate, "History of Mammography: Analysis of Breast Imaging Diagnostic Achievements over the Last Century.," Healthcare, vol. 11, no. 11, p. 1596, 2023.
[6] F. Shahidi, S. Daud, H. Abas, N. Ahmad and N. Maarop, "Breast Cancer Classification using Deep Learning Approaches and Histopathology Image: A Comparison Study," IEEE Access, vol. 8, pp. 187531-187552, 2020.
[7] B. Bektaş, İ. Emre, E. Kartal and S. Gulsecen, "Classification of Mammography Images by Machine Learning Techniques," in 3rd International Conference on Computer Science and Engineering (UBMK) (pp. 580-585), 2018.
[8] J. Arevalo, F. González, R. Ramos-Pollán, J. Oliveira and M. Lopez, "Convolutional Neural Networks for Mammography Mass Lesion Classification," in 37th Annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp. 797-800). IEEE, 2015.
[9] H. Li, S. L. Zhuang, Z. D.A. and Y. J. Ma, "Benign and Malignant Classification of Mammogram Images based on Deep Learning," Biomedical Signal Processing and Control, vol. 51, pp. 347-354, 2019.
[10] P. Hepsağ, S. Özel and A. Yazıcı, "Using Deep Learning for Mammography Classification," in International Conference on Computer Science and Engineering (UBMK) (pp. 418-423). IEEE, 2017.
[11] W. Salama and M. Aly, "Deep Learning in Mammography Images Segmentation and Classification: Automated CNN Approach," Alexandria Engineering Journal, vol. 60, no. 5, pp. 4701-4709, 2021.
[12] U. Albalawi, S. Manimurugan and R. Varatharajan, "Classification of Breast Cancer Mammogram Images using Convolution Neural Network," Concurrency and Computation: Practice and Experience, vol. 34, no. 13, p. 5803, 2022.
[13] T. Mahmood, J. Li, Y. Pei, F. Akhtar, M. Rehman and S. Wasti, "Breast Lesions Classifications of Mammographic Images Using a Deep Convolutional Neural Network-based Approach," Plos One, vol. 17, no. 1, 2022.
[14] S. Agnes, J. Anitha, S. Pandian and J. Peter, "Classification of Mammogram Images using Multiscale All Nonvolutional Neural Network (MA-CNN)," Journal of Medical Systems, vol. 44, no. 1, p. 30, 2019.
[15] U. K. Sajid, R.A., S. S.M. and S. Arif, "Breast Cancer Classification using Deep Learned Features Boosted with Handcrafted Features," Biomedical Signal Processing and Control, , vol. 86, 2023.
[16] A. Joseph, M. Abdullahi, S. Junaidu, H. Ibrahim and H. Chiroma, "Improved Multi-classification of Breast Cancer Histopathological Images using Handcrafted Features and Deep Neural Network (dense layer)," Intelligent Systems with Applications, vol. 14, 2022.
[17] D. Muduli, R. Dash and B. Majhi, "Automated Diagnosis of Breast Cancer using Multi-modal Datasets: A Deep Convolution Neural Network based Approach," Biomedical. Signal Processing and Control, vol. 71, 2022.
[18] L. Falconí, M. Pérez, W. Aguilar and A. Conci, "Transfer Learning and Fine Tuning in Mammogram Bi-rads Classification," in 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), Rochester, MN, USA., 2020.
[19] C. Szegedy, S. Ioffe, V. Vanhoucke and A. A. Alemi, "Inception-v4, Inception-Resnet and the Impact of Residual Connections on Learning," in Thirty-first AAAI Conference on Artificial Intelligence, San Francisco, 2017.
[20] B. Jena, G. K. Nayak and S. Saxena, "Convolutional Neural Network and Its Pretrained Models for Image Classification and Object Detection: A Survey," Concurrency and Computation: Practice and Experience, vol. 34, no. 6, 2022.
[21] A. Caroppo, A. Leone and P. Siciliano, "Deep Transfer Learning Approaches for Bleeding Detection in Endoscopy Images," Computerized Medical Imaging and Graphics, vol. 88, p. 101852, 2021.
[22] C. Ding and P. H., "Minimum Redundancy Feature Selection from Microarray Gene Expression Data," Journal of Bioinformatics and Computational Biology, vol. 3, no. 2, p. 185–205, 2005.
[23] M. M. AlyanNezhadi, H. Dabbaghan, S. Moghani and M. Forghani, A Painting Artist Recognition System Based on Image Processing and Hierarchical SVM, Tehran, Iran: 5th Conference on Knowledge Based Engineering and Innovation (KBEI), 2019.
[24] P. S. Sundaram and N. Santhiyakumari, "An Enhancement of Computer Aided Approach for Colon Cancer Detection in WCE Images Using ROI Based Color Histogram and SVM2," Journal of medical systems, vol. 43, no. 2, p. 29, 2019.
[25] Z. Amiri, H. Hassanpour and A. Beghdadi, "Abnormalities detection in wireless capsule endoscopy images using EM algorithm," The Visual Computer, vol. 39, no. 7, pp. 2999-3010, 2023.
[26] Z. Amiri, H. Hassanpour and A. Beghdadi, "Combining deep features and hand-crafted features for abnormality detection in WCE images," Multimedia Tools and Applications, vol. 83, no. 2, pp. 5837-5870, 2024.
