{"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-classification-from","title":"Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network","arxiv_id":"1811.04241","date":"2018-11-10","proceeding":null,"authors":["Md Zahangir Alom","Chris Yakopcic","Tarek M. Taha","Vijayan K. Asari"],"abstract":"The Deep Convolutional Neural Network (DCNN) is one of the most powerful and\nsuccessful deep learning approaches. DCNNs have already provided superior\nperformance in different modalities of medical imaging including breast cancer\nclassification, segmentation, and detection. Breast cancer is one of the most\ncommon and dangerous cancers impacting women worldwide. In this paper, we have\nproposed a method for breast cancer classification with the Inception Recurrent\nResidual Convolutional Neural Network (IRRCNN) model. The IRRCNN is a powerful\nDCNN model that combines the strength of the Inception Network (Inception-v4),\nthe Residual Network (ResNet), and the Recurrent Convolutional Neural Network\n(RCNN). The IRRCNN shows superior performance against equivalent Inception\nNetworks, Residual Networks, and RCNNs for object recognition tasks. In this\npaper, the IRRCNN approach is applied for breast cancer classification on two\npublicly available datasets including BreakHis and Breast Cancer Classification\nChallenge 2015. The experimental results are compared against the existing\nmachine learning and deep learning-based approaches with respect to\nimage-based, patch-based, image-level, and patient-level classification. The\nIRRCNN model provides superior classification performance in terms of\nsensitivity, Area Under the Curve (AUC), the ROC curve, and global accuracy\ncompared to existing approaches for both datasets.","url_abs":"http://arxiv.org/abs/1811.04241v1","url_pdf":"http://arxiv.org/pdf/1811.04241v1.pdf","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-classification-from","repo_url":"https://github.com/manhcuongk55/gender-classification-by-hand","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"cancer-classification","task_name":"Cancer Classification"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"object-recognition","task_name":"Object Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.04241","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.04241"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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