Prostate Cancer Gleason Score Classification Using Transfer Learning Models

Abstract

This research discusses the application of transfer learning models in the classification of prostate cancer based on Gleason scores. Gleason scoring is crucial in determining the aggressiveness of prostate cancer, guiding treatment decisions. Transfer learning, a technique where knowledge from one task is applied to another, has gained traction in medical image analysis. This study explores the efficacy of transfer learning models, such as convolutional neural networks (CNNs), in accurately classifying Gleason scores from histopathological images. Leveraging pre-trained CNNs like ResNet and VGG, the research demonstrates significant improvements in classification accuracy compared to traditional machine learning approaches. The methodology involves fine-tuning these pre-trained models on a dataset of prostate cancer histopathological images annotated with Gleason scores. Experimental results showcase promising performance metrics, including high accuracy, precision, recall, and F1-score, highlighting the potential of transfer learning in enhancing prostate cancer diagnosis and prognostication. This work contributes to the growing body of research utilizing deep learning techniques for improving cancer classification and personalized treatment strategies.