Breast Cancer Detection using Histopathological Images

TL;DR

This paper presents a deep learning-based saliency detection system for early breast cancer diagnosis using histopathological images, aiming to assist pathologists in localizing cancerous regions effectively.

Contribution

It introduces a CNN-based approach utilizing VGG16 and ResNet architectures trained on BreakHis dataset for detecting and classifying breast cancer regions in histopathology images.

Findings

Achieved accurate localization of cancerous regions.

Demonstrated effective classification of five diagnostic categories.

System will be available as an open source web application.

Abstract

Cancer is one of the most common and fatal diseases in the world. Breast cancer affects one in every eight women and one in every eight hundred men. Hence, our prime target should be early detection of cancer because the early detection of cancer can be helpful to cure cancer effectively. Therefore, we propose a saliency detection system with the help of advanced deep learning techniques, such that the machine will be taught to emulate actions of pathologists for localization of diagnostically pertinent regions. We study identification of five diagnostic categories of breast cancer by training a CNN (VGG16, ResNet architecture). We have used BreakHis dataset to train our model. We focus on both detection and classification of cancerous regions in histopathology images. The diagnostically relevant regions are salient. The detection system will be available as an open source web application which can be used by pathologists and medical institutions.