Leveraging Transfer Learning for High-Accuracy Breast Cancer Classification from Histopathological Images

Overview

This study explores transfer learning for classifying breast cancer from histopathological images. It adapts visual features learned from broader image datasets to tissue microscopy, aiming to make better use of labeled medical images and recognize patterns associated with cancer classes. The work evaluates this approach as a practical route toward computer-assisted analysis of breast tissue.