A Hierarchical Deep Learning Framework for Prostate Cancer Classifcation Using Swin Transformer V2

Urological health is a global concern since urologic cancers that remain undiagnosed and untreated, mostly kidney, bladder, and prostate cancer, can lead to ghastly physical, psychological, and economic devastation. The cancers progress in stealth mode with minimal prodromal symptoms until far advanced in the disease process; thus, early detection becomes a major concern. This project strives to construct a strong and interpretable deep learning model in order to facilitate early silent urological cancer detection. This paper introduces a new method built upon the Swin Transformer V2 model, where it uses its hierarchical attention and self-supervised learning mechanism to learn high-resolution pathological patterns at various scales. The model is learnt on a very large, properly annotated medical image dataset of more than 100,000 images with rigorous preprocessing such as resizing, normalising, and intensive data augmentation. The goal is to achieve higher diagnostic accuracy at the cost of not sacrificing generalisability and clinical robustness. Experimental evidence shows that the model performs much better than usual CNN and FCNN baselines with higher classification accuracy and F1 scores. The above results confirm the potential of the model as a useful device for AI-augmented screening and diagnosis of urology cancers, thus leading to early treatment and improved outcomes for patients.