{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/breast-cancer-histology-classification-using","title":"Breast cancer histology classification using Deep Residual Networks","arxiv_id":null,"date":"2018-07-01","proceeding":"Engineering in Medicine and Biology Society (EMBC), 2018 40th Annual International Conference of the IEEE 2018 7","authors":["Kamalakkannan Ravi","Sakthivel Selvaraj","JM Poorneshwaran","Keerthi Ram","Mohanasankar Sivaprakasam"],"abstract":"In this work, in order to improve the computer aided diagnosis systems’ performance on histopathological image analysis, we have proposed an approach with image pre-processing followed by a deep learning method to classify the breast cancer histology images into four classes; (i) normal tissue, (ii) benign lesion, (iii) in-situ carcinoma, and (iv) invasive carcinoma. The images are preprocessed for intensity and stain normalization using histogram equalization method. The Fine-tuning ConvNet transfer learning method is used with ResNet152 to train and classify the images. This proposed approach yields an average fivefold cross validation accuracy of 83%, a substantial improvement over the state-of-the-art.","url_abs":"https://www.researchgate.net/publication/374471906_Breast_cancer_histology_classification_using_Deep_Residual_Networks","url_pdf":"https://www.researchgate.net/publication/374471906_Breast_cancer_histology_classification_using_Deep_Residual_Networks","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"breast-cancer-histology-classification-using","repo_url":"https://github.com/kamalravi/Lung-cancer-detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"breast-cancer-histology-image-classification","task_name":"Breast Cancer Histology Image Classification"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/breast-cancer-histology-image-classification-2","task":"Breast Cancer Histology Image Classification","dataset":"ICIAR 2018 Grand Challenge on Breast Cancer Histology Images","model":"ResNet-152","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy (% )":"83"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}