Oral cancer (OC) remains a major global health concern with survival often limited by late diagnosis. Early and accurate detection is essential to improve patient outcomes and guide treatment decisions. In this study we propose a computer aided diagnostic (CAD) framework for classifying oral squamous cell carcinoma from histopathology images. The model combines Swin transformer for hierarchical feature extraction with vision transformer (ViT) to capture long range dependencies across image regions. SHapley Additive exPlanations (SHAP) based feature selection enhances interpretability by highlighting the most informative features while preprocessing steps such as stain normalization and contrast enhancement improve model generalization and reduce sample variability. Evaluated on a publicly available dataset the framework achieved 99.25% accuracy (ACC) 99.21% sensitivity and a matthews correlation coefficient (MCC) of 98.21% outperforming existing methods. Ablation studies highlighted the importance of positional encoding and statistical analyses confirmed the robustness and reliability of results. To support real-time inference and scalable deployment the proposed model has been integrated into a FastAPI-based web application. This framework offers a powerful interpretable and practical tool for early OC detection and has potential for integration into routine clinical workflows.

Ovarian cancer (OC) and polycystic ovary syndrome (PCOS) significantly impact the lives of millions of women worldwide. PCOS affects 8-13% of women of reproductive age, while OC is regarded as the deadliest form of gynecological cancer. Their impacts are enormous, yet the molecular links between these two diseases are not well defined. This study tries to explore this important knowledge gap using an integrated bioinformatics approach to unravel common genetic signatures and potential therapeutic candidates for PCOS and OC. We used differential gene expression (DEGs) analysis, protein-protein interaction (PPI) networks, pathway enrichment analysis, and regulatory network analysis on two microarray datasets. Utilizing the sophisticated machine learning approach known as minimum redundancy maximum relevance (mRMR), we detected 24 genes that exhibited differential expression across both conditions. Analysis of the PPI network highlighted ten prominent hub genes - GIGYF2, FGB, ZEB2, S100P, TNFRSF10C, CP, LMAN2, RAPH1, UBC, and KDM4D - which are presumably integral to the molecular interactions between PCOS and OC. Functional enrichment analysis identified significant biological pathways; the regulatory network analysis revealed complex TFs, miRNAs, and DEGs interactions. More importantly, we have predicted potential therapeutic compounds apigenin and sirolimus targeting the shared DEGs. These results provide a crucial clue toward the possible molecular underlying mechanisms of PCOS and OC for further therapeutic and diagnostic interventions. The present study is epitomizing the effectiveness of integrating different bioinformatic approaches to uncover complex disease mechanisms and guide further investigational efforts in the field of women's health.