Deep Learning ResNet Model for Prostate Cancer Classification: A Comparative Study of CNNs

Prostate cancer diagnosis has been transformed by the integration of machine learning techniques, particularly deep learning models. This study focuses on utilizing ResNet for Gleason score prediction from multi-parametric MRI (mpMRI) and comparing its performance with other CNN architectures. We explore feature selection, classification techniques, and optimization strategies to improve accuracy. Both supervised and unsupervised learning approaches are analyzed to enhance detection and grading. Experimental results demonstrate ResNet’s superiority in capturing complex imaging features, thereby improving classification performance and supporting clinical decision-making.