Abstract
Breast cancer is defined as abnormal cellular proliferation in the breast. The most common kind of cancer that affects the breast and causes mortality in women is invasive ductal carcinoma (IDC). As a result, early diagnosis and prognosis have become critical to maximize survival and minimize mortality. Mammograms, computerized tomography (CT) scans, and ultrasounds are among the breast cancer tests available. On the other hand, histopathology evaluation with a biopsy is regarded as one of the most trustworthy techniques for determining if suspicious lesions are malignant. This paper proposes an enhanced approach for classifying breast tumors using a proposed CNN model that we named (CancerNet). We evaluate the proposed model on a benchmark dataset containing 277,524 patches. Compared to several types of CNN-based models, our proposed model has achieved accuracy, Area Under Curve (AUC), precision, recall, and F1-score of 86%, 92%, 81%, 84%, and 83%, respectively, outperforming the previous work on the same benchmark.
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References
Goyal, K., Sodhi, P., Aggarwal, P., Kumar, M.: Comparative analysis of machine learning algorithms for breast cancer prognosis. In: Krishna, C.R., Dutta, M., Kumar, R. (eds.) Proceedings of 2nd International Conference on Communication, Computing and Networking. LNNS, vol. 46, pp. 727–734. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1217-5_73
Cao, W., Chen, H.-D., Yu, Y.-W., Li, N., Chen, W.-Q.: Changing profiles of cancer burden worldwide and in china: a secondary analysis of the global cancer statistics 2020. Chin. Med. J. 134(07), 783–791 (2021)
Globocan 2018: India factsheet—cancerindia.org.in. http://cancerindia.org.in/globocan-2018-india-factsheet/
Chugh, G., Kumar, S., Singh, N.: Survey on machine learning and deep learning applications in breast cancer diagnosis. Cogn. Comput. 13(6), 1451–1470 (2021)
Khuriwal, N., Mishra, N.: Breast cancer diagnosis using deep learning algorithm. In: 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), pp. 98–103. IEEE (2018)
Selvathi, D., Aarthy Poornila, A.: Deep learning techniques for breast cancer detection using medical image analysis. In: Hemanth, J., Balas, V.E. (eds.) Biologically Rationalized Computing Techniques For Image Processing Applications. LNCVB, vol. 25, pp. 159–186. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-61316-1_8
Lai, Z., Deng, H.: Medical image classification based on deep features extracted by deep model and statistic feature fusion with multilayer perceptron. Comput. Intell. Neurosci. 2018 (2018)
Duggento, A., et al.: A random initialization deep neural network for discriminating malignant breast cancer lesions. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 912–915. IEEE (2019)
Alkhouly, A.A., Mohammed, A., Hefny, H.A.: Improving the performance of deep neural networks using two proposed activation functions. IEEE Access 9, 82249–82271 (2021)
Mohammed, A., Kora, R.: An effective ensemble deep learning framework for text classification. J. King Saud Univ.-Comput. Inf. Sci. 34(10), 8825–8837 (2022)
Rashmi, R., Prasad, K., Udupa, C.B.K.: Breast histopathological image analysis using image processing techniques for diagnostic puposes: a methodological review. J. Med. Syst. 46(1), 1–24 (2022)
Eldin, S.N., Hamdy, J.K., Adnan, G.T., Hossam, M., Elmasry, N., Mohammed, A.: Deep learning approach for breast cancer diagnosis from microscopy biopsy images. In: 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), pp. 216–222 (2021)
Cruz-Roa, A., et al.: Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. In: Medical Imaging 2014: Digital Pathology, vol. 9041, p. 904103. SPIE (2014)
Rosebrock, A.: Breast cancer classification with Keras and Deep Learning - PyImageSearch—pyimagesearch.com. https://pyimagesearch.com/2019/02/18/breast-cancer-classification-with-keras-and-deep-learning/
Araújo, T., et al.: Classification of breast cancer histology images using convolutional neural networks. PloS One 12(6), e0177544 (2017)
Roy, K., Banik, D., Bhattacharjee, D., Nasipuri, M.: Patch-based system for classification of breast histology images using deep learning. Comput. Med. Imaging Graph. 71, 90–103 (2019)
Li, Y., Wu, J., Wu, Q.: Classification of breast cancer histology images using multi-size and discriminative patches based on deep learning. IEEE Access 7, 21400–21408 (2019)
Deniz, E., Şengür, A., Kadiroğlu, Z., Guo, Y., Bajaj, V., Budak, Ü.: Transfer learning based histopathologic image classification for breast cancer detection. Health Inf. Sci. Syst. 6(1), 1–7 (2018). https://doi.org/10.1007/s13755-018-0057-x
Brancati, N., De Pietro, G., Frucci, M., Riccio, D.: A deep learning approach for breast invasive ductal carcinoma detection and lymphoma multi-classification in histological images. IEEE Access 7, 44709–44720 (2019)
Jiang, Y., Chen, L., Zhang, H., Xiao, X.: Breast cancer histopathological image classification using convolutional neural networks with small se-resnet module. PloS One 14(3), e0214587 (2019)
Nahid, A.-A., Mehrabi, M.A., Kong, Y.: Histopathological breast cancer image classification by deep neural network techniques guided by local clustering. BioMed Res. Int. 2018 (2018)
Yan, R., et al.: Integration of multimodal data for breast cancer classification using a hybrid deep learning method. In: Huang, D.-S., Bevilacqua, V., Premaratne, P. (eds.) ICIC 2019. LNCS, vol. 11643, pp. 460–469. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26763-6_44
Sharma, S., Mehra, R.: Conventional machine learning and deep learning approach for multi-classification of breast cancer histopathology images-a comparative insight. J. Dig. Imaging 33(3), 632–654 (2020)
Kumar, K., Saeed, U., Rai, A., Islam, N., Shaikh, G.M., Qayoom, A.: Idc breast cancer detection using deep learning schemes. Adv. Data Sci. Adapt. Anal. 12(02), 2041002 (2020)
Janowczyk, A., Madabhushi, A.: Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J. Pathol. Inf. 7(1), 29 (2016)
He, Y., Qian, J., Wang, J.: Depth-wise decomposition for accelerating separable convolutions in efficient convolutional neural networks. arXiv preprint arXiv:1910.09455 (2019)
Breast Histopathology Images—kaggle.com. https://www.kaggle.com/datasets/paultimothymooney/breast-histopathology-images
Abdolahi, M., Salehi, M., Shokatian, I., Reiazi, R.: Artificial intelligence in automatic classification of invasive ductal carcinoma breast cancer in digital pathology images. Med. J. Islamic Rep. Iran 34, 140 (2020)
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Ouf, M., Abdul-Hamid, Y., Mohammed, A. (2023). An Enhanced Deep Learning Approach for Breast Cancer Detection in Histopathology Images. In: Hassanien, A.E., et al. The 3rd International Conference on Artificial Intelligence and Computer Vision (AICV2023), March 5–7, 2023. AICV 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 164. Springer, Cham. https://doi.org/10.1007/978-3-031-27762-7_3
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