Multiclass Classification of Histology Images of Breast Cancer Using Improved Deep Learning Approach

Breast Cancer has become a serious threat to women life in the overall word. It is necessary to diagnosis for Breast cancer early to avoid mortality rate. In the era of medical imagining with the advent of Artificial Intelligence systems, it has become easy to detect breast cancer at an early stage. Histopathology image modality is one of the best ways for breast cancer detection as it is easy to store in digital format for a long time. Breast cancer is classified into two main categories one is benign and malignant. These two classes are further divided into subclasses. In this paper, we have proposed an improved deep learning model for breast cancer multiclass classification. This proposed model uses a two-level approach one is blocked based and image-based. The blocked based approach is used to reduce overhead with lower-cost processing. Here ensemble learning approach is used for feature extraction which is a combined approach of the various pre-trained model without negotiating accuracy of the system. Final classification is done based on image based improved deep learning approach into eight classes.