Breast Cancer Image Classification using Convolutional Neural Networks (CNN) Models

Abstract

Breast cancer can progress silently in its early stages and frequently without noticeable symptoms. However, it poses a serious risk to women. It is imperative to recognize this potential health concern to mitigate it early. In the last few years, Convolutional Neural Networks (CNNs) have advanced significantly in their ability to classify images of breast cancer. Their capacity to automatically extract discriminant features from images has enhanced the performances and accuracy of image classification tasks. They outperform state-of-the-art techniques in this area. Furthermore, complicated models that were first learned for certain tasks can be easily adapted to complete new tasks by using transfer-learning approaches. However, deep learning-based categorization techniques could experience overfitting issues, particularly in cases where the dataset is small. The primary goal of this work is to investigate the performances of certain deep learning models to classify breast cancer images and to study the effects of data augmentation techniques, such as image rotation or displacement when utilizing a transfer learning approach. Using certain image datasets, the ResNet18, Resnet50, and VGG16 models demonstrated accuracy improvements, according to our experimental results.

Keywords

• CNN
• ResNet18
• Resnet50
• VGG16
• Data Augmentation
• Transfer Learning

References

1. Mohammed Amine Naji, Sanaa El Filali, Kawtar Aarika, EL Habib Benlahmar, Rachida Ait Abdelouhahid, and Olivier Debauche. Machine learning algorithms for breast cancer prediction and diagnosis. Procedia Computer Science, 191:487-492, 2021.
2. Yahya Alqahtani, Umakant Mandawkar, Aditi Sharma, Mohammad Najmus Saquib Hasan, Mrunalini Harish Kulkarni, and R Sugumar. Breast cancer pathological image classification based on the multiscale cnn squeeze model. Computational Intelligence and Neuroscience, 2022, 2022.
3. Fei Gao, Teresa Wu, Jing Li, Bin Zheng, Lingxiang Ruan, Desheng Shang, and Bhavika Patel. Sd-cnn: A shallow-deep cnn for improved breast cancer diagnosis. Computerized Medical Imaging and Graphics, 70:53-62, 2018.
4. Nasser Edinne Benhassine, Abdelnour Boukaache, and Djalil Boudjehem. Classification of mammogram images using the energy probability in frequency domain and most discriminative power coefficients. International Journal of Imaging Systems and Technology, 30(1):45-56, 2020.
5. Rikiya Yamashita, Mizuho Nishio, Richard Kinh Gian Do, and Kaori Togashi. Convolutional neural networks: an overview and application in radiology. Insights into imaging, 9:611-629, 2018.
6. June-Goo Lee, Sanghoon Jun, Young-Won Cho, Hyunna Lee, Guk Bae Kim, Joon Beom Seo, and Namkug Kim. Deep learning in medical imaging: general overview. Korean journal of radiology, 18(4):570-584, 2017.
7. Shallu Sharma and Rajesh Mehra. Conventional machine learning and deep learning approach for multi-Classification of breast cancer histopathology images a comparative insight. Journal of digital imaging, 33:632-654, 2020.
8. Hasnia Merzoug, Hayat Yedjour, and Samira Chouraqui. A deep system for breast tumor Classification from mammograms. International Journal on Communications Antenna and Propagation (IRECAP), 12(6), 2022. doi:10.15866/irecap.v12i6.22300.

9. Shreyansh A. Prajapati, R. Nagaraj, and Suman Mitra. Classification of dental diseases using cnn and transfer learning. In 2017 5th International Symposium on Computational and Business Intelligence (ISCBI), pages 70-74, 2017.
10. Duyen NT Le, Hieu X Le, Lua T Ngo, and Hoan T Ngo. Transfer learning with class-weighted and focal loss function for automatic skin cancer Classification. arXiv preprint arXiv:2009.05977, 2020.
11. S Sasikala, SN Shivappriya, et al. Towards improving skin cancer detection using transfer learning. Biosci Biotechnol Res Commun, 13(11):55-60, 2020.
12. Enas MF El Houby. Using transfer learning for diabetic retinopathy stage Classification . Applied Computing and Informatics, 2021.
13. Honey Janoria, Jasmine Minj, and Pooja Patre. Classification of skin disease from skin images using transfer learning technique. In 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), pages 888-895, 2020.
14. Weonsuk Lee, Hyeonsoo Lee, Hyunjae Lee, Eun Kyung Park, Hyeonseob Nam, and Thijs Kooi. Transformer-based deep neural network for breast cancer Classification on digital breast tomosynthesis images. Radiology: Artificial Intelligence, 5(3):e220159, 2023.
15. Mobarak Zourhri, Soufiane Hamida, Nouhaila Akouz, Bouchaib Cherradi, Hasna Nhaila, and Mohamed El Khaili. Deep learning technique for Classification of breast cancer using ultrasound images. In 2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), pages 1-8, 2023.
16. Wen Yu and Yanqiu Li. Breast cancer classification from histopathological images using transformers. In Thirteenth International Conference on Information Optics and Photonics (CIOP 2022), volume 12478, pages 562-569. SPIE, 2022.
17. Abeer Saber, Mohamed Sakr, Osama M Abo-Seida, Arabi Keshk, and Huiling Chen. A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique. IEEE Access, 9:71194-71209, 2021.
18. Khalid M Hosny, Mohamed A Kassem, and Mohamed M Fouad. Classification of skin lesions into seven classes using transfer learning with alexnet. Journal of digital imaging, 33:1325-1334, 2020.
19. Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
20. Alaa MA Barhoom, MOHAMMED RASHEED J Al-Hiealy, and SAMY S AbuNaser. Bone abnormalities detection and classification using deep learning vgg16 algorithm. Journal of Theoretical and Applied Information Technology, 100(20):6173{6184, 2022.
21. Srikanth Tammina. Transfer learning using vgg-16 with deep convolutional neural network for classifying images. International Journal of Scientific and Research Publications (IJSRP), 9(10):143-150, 2019.
22. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.
23. Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, pages 448-456. pmlr, 2015.
24. Bo Huang, Jianhong Liu, Qian Zhang, Kang Liu, Kun Li, and Xinyu Liao. Identification and classification of aluminum scrap grades based on the resnet18 model. Applied Sciences, 12(21):11133, 2022.
25. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.
26. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.
27. Nurul Ashikin Samat, Mohd Najib Mohd Salleh, and Haseeb Ali. The comparison of pooling functions in convolutional neural network for sentiment analysis task. In Recent Advances on Soft Computing and Data Mining: Proceedings of the Fourth International Conference on Soft Computing and Data Mining (SCDM 2020), Melaka, Malaysia, 2020, pages 202-210. Springer, 2020.
28. SR Paulo. Breast ultrasound image. mendeley data, 2017.
29. Walid Al-Dhabyani, Mohammed Gomaa, Hussien Khaled, and Aly Fahmy. Dataset of breast ultrasound images. Data in brief, 28:104863, 2020.
30. Mahesh Gour, Sweta Jain, and T Sunil Kumar. Residual learning based cnn for breast cancer histopathological image classification . International Journal of Imaging Systems and Technology, 30(3):621-635, 2020.
31. Yaqi Wang, Lingling Sun, Kaiqiang Ma, and Jiannan Fang. Breast cancer microscope image classification based on cnn with image deformation. In Image Analysis and Recognition: 15th International Conference, ICIAR 2018, Povoa de Varzim, Portugal, June 27-29, 2018, Proceedings 15, pages 845-852. Springer, 2018.
32. Erkan Deniz, Abdulkadir Sengur, Zehra Kadiroglu, Yanhui Guo, Varun Bajaj, and Umit Budak. Transfer learning based histopathologic image classification for breast cancer detection. Health information science and systems, 6:1-7, 2018.
33. Caroline B Goncalves, Jeerson R Souza, and Henrique Fernandes. Classification of static infrared images using pre-trained cnn for breast cancer detection. In 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), pages 101-106. IEEE, 2021.