Transfer Learning based Breast Cancer Classification using Histopathology Images

Breast cancer is a significant global health concern, and early detection is crucial for improving patient outcomes. This study explores the potential of Deep Learning (DL) models, including InceptionNet, ResNet, and EfficientNet, for improving the accuracy of breast cancer classification from histopathological images. Leveraging the ICIAR2018-Challenge BACH (Breast Cancer Histology Images) dataset, these models' performances are evaluated and their results are compared, leading to the findings indicating that EfficientNet outperforms the other models, achieving an accuracy of 89.2%.