{"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/vggin-net-deep-transfer-network-for","title":"VGGIN-Net: Deep Transfer Network for Imbalanced Breast Cancer Dataset","arxiv_id":null,"date":"2022-03-29","proceeding":"IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022 3","authors":["Manisha Saini","Seba Susan"],"abstract":"In this paper, we have presented a novel deep neural network architecture involving transfer learning approach, formed \r\nby freezing and concatenating all the layers till block4 pool layer of VGG16 pre-trained model (at the lower level) with the layers \r\nof a randomly initialized naïve Inception block module (at the higher level). Further, we have added the batch normalization, flatten, \r\ndropout and dense layers in the proposed architecture. Our transfer network, called VGGIN-Net, facilitates the transfer of domain \r\nknowledge from the larger ImageNet object dataset to the smaller imbalanced breast cancer dataset. To improve the performance of \r\nthe proposed model, regularization was used in the form of dropout and data augmentation. A detailed block-wise fine tuning has \r\nbeen conducted on the proposed deep transfer network for images of different magnification factors. The results of extensive \r\nexperiments indicate a significant improvement of classification performance after the application of fine-tuning. The proposed deep \r\nlearning architecture with transfer learning and fine-tuning yields the highest accuracies in comparison to other state-of-the-art \r\napproaches for the classification of BreakHis breast cancer dataset. The articulated architecture is designed in a way that it can be \r\neffectively transfer learned on other breast cancer datasets.","url_abs":"https://ieeexplore.ieee.org/document/9744541","url_pdf":"https://drive.google.com/file/d/1catVKX8IgJX_aTLfgS72JUCmTsNBXE3m/view?usp=sharing","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":"vggin-net-deep-transfer-network-for","repo_url":"https://github.com/SainiManisha/VGGIN-Net","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"breast-cancer-detection","task_name":"Breast Cancer Detection"},{"task_slug":"breast-cancer-histology-image-classification","task_name":"Breast Cancer Histology Image Classification"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[{"method_slug":"dropout","method_name":"Dropout"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/breast-cancer-histology-image-classification","task":"Breast Cancer Histology Image Classification","dataset":"BreakHis","model":"VGGIN-Net","rank_in_archive_order":3,"of":5,"metrics":{"Accuracy (%)":"96.15"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}