VISUAL CORRESPONDENCE-BASED EXPLANATIONS IMPROVE CONVOLUTIONAL NEURAL NETWORKSFOR CLASSIFICATIONOF MAMMOGRAMS
- Research Scholar, Institute of Information Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam
- Associate Professor, Faculty of Information Technology, Thang Long University, Hanoi, Vietnam.
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Explainable Artificial Intelligence (XAI) classifications are increasingyimportant in convolutional neural networks for the classification of mammograms. In this paper, we propose a novel architecture of Visual Correspondence Based Explanations that improve Convolutional Neural Networks (cnns) for Classification of Mammograms of self-interpretable image classifiers that first explain, and then predict by harnessing the visual correspondences between a query breast cancer X-Ray image and exemplars. In the evaluation of our proposed models, the k-nearest neighbor (knn) classifier improves upon ResNet-18 on Breast Cancer datasets.
[Ha Manh Toan and Nguyen Hoang Phuong (2025); VISUAL CORRESPONDENCE-BASED EXPLANATIONS IMPROVE CONVOLUTIONAL NEURAL NETWORKSFOR CLASSIFICATIONOF MAMMOGRAMS Int. J. of Adv. Res. (Aug). 1273-1281] (ISSN 2320-5407). www.journalijar.com
Faculty of Information Technology, Thang Long University, Hanoi, Vietnam.
Viet Nam
