A Novel Profound Learning-Based System for Breast Cancer Location and Classification Utilizing Exchange Learning

Abstract

Breast cancer is the leading cause of death in Asian women. Breast cancer can be detected early and treated aggressively. In this paper, we propose a new deep learning framework utilizing the index learning concept for breast cancer diagnosis and imaging. Typically, in-depth questions from experts are designed and presented in a specific format. Unlike classical learning paradigms, which are based on parallel development and emergence, the goal is to use the knowledge gained from solving one problem to solve another problem. In this study, we used CNN pretrainers to extract features from images, such as Google Net, VGGNet (Visual Geometry Group Network), and ResNet (Unsupervised Network), which combine the principles of neural networks and social networks. This has already happened. Contains partial information. Use the blunt treasure in the veil. A standard data model was used to evaluate the proposed optical products. Chemical images of different types of breast cancer have been discovered in archaeological finds and have proven useful in educational exploration into the unknown.