A GUI Based Application for Breast Cancer Diagnosis from Histopathology Images Using a Sequential Convolutional Neural Network Model

Breast cancer is the most common type of cancer among women globally. Automated breast cancer diagnosis improves healthcare by saving time and providing valuable assistance to pathologists. The focus of this research is the development of a deep learning framework named BCnet (Breast Cancer net), which aims to automatically diagnose breast cancer using histopathological images at various levels of magnification. We train the model from scratch using the BreakHis dataset, incorporating augmentation techniques. BCnet's performance is subsequently evaluated against current architectures that utilize the transfer learning approach. Furthermore, a Graphical User Interface (GUI) application was specifically designed for pathologists to use. BCnet was 95% accurate, while VGG-16, VGG-19, Xception, InceptionResnetV2, and Resnet152V2 were 78%, 79%, 77%, 79%, and 84% accurate, respectively. BCnet hence functions as an intelligent assistance system for pathologists and a potent tool for timely and precise identification, with the capacity to significantly influence patient outcomes.