SLIDE-Net: A Sequential Modeling Approach with Adaptive Fuzzy C-Mean Empowered Data Balancing Policy for IDC Detection

Breast cancer is a significant global health concern, with Invasive Ductal Carcinoma (IDC) being a significant subtype. Detecting IDC is a challenging task that can be hindered by the oversight of important contextual cues within Whole Slide Images (WSIs). To address this issue, we present the SequentialLSTM Invasive Ductal Carcinoma Detection with EfficientNet (SLIDE-Net) framework. SLIDE-Net synchronizes tissue image patch locations within WSIs, allowing for comprehensive and subtle identification of IDC. The inherent issue of data imbalance in IDC datasets, particularly the variable density of positive IDC patches concerning WSI size, is effectively tackled through the introduction of Adaptive Fuzzy C-Mean as a Data Balancing Policy. This novel approach enhances model efficacy and robustness, resulting in superior performance across key metrics such as accuracy (86%), balanced accuracy (86%), sensitivity (87%), specificity (86%), and GMean (86%). Our findings were substantiated by a comprehensive analysis revealing the significant cross-patch dependencies observed among patches of similar classes. When tested on the balanced PatchCAM dataset, SLIDENet once again showcased superiority with accuracy (89%), balanced accuracy (89%), F1-Score (87%), sensitivity (87%), and specificity (90%). This underscores the efficient utilization of shared contextual information within WSIs. These results firmly establish SLIDE-Net as a robust and reliable solution for accurate IDC detection. Our work not only advances the precision of IDC detection but also contributes valuable insights into the intricate dynamics of histopathology image analysis, paving the way for enhanced diagnostic accuracy in the ongoing battle against breast cancer. Index Terms—Breast Cancer, Sequential-Modeling, Data Balancing Policy, WSI, Adaptive Fuzzy C-Mean, Deep Learning.