Breast Cancer Histopathological Images Multi-classification using Deep Learning
NETI PRAVEEN, Dr. Subhani Shaik, Dr. SVVSR Kumar Pullela
Breast Cancer, Deep Features, Deep Learning, Histopathological image, Multi-classification and Segmentation
In recent years, breast cancer classification can be considered a primary subject for biology and health care, given that cancer is the second leading cause of death in women. From there, the medical community has seen advances in the field of research in the use of various techniques to screen for and identify multifold threatening diseases, such as breast cancer. In this survey, the various deep learning (DL) approaches are analyzed for multi-classifying the breast cancer histopathological images. This survey discusses the significant assumptions, limitations, and advantages are analyzed in existing DL based techniques as segmentation and classification are used for multi-classification of breast cancer histopathological images. The existing method’s performance was analyzed by using various performance measures such as accuracy, precision, recall, sensitivity, specificity and f1-score. This survey concludes that the various breast cancer histopathological image multi-classification over DL have feasible to overcome the drawbacks as inefficient supervised feature and enhance the efficiency.
Article Details
Unique Paper ID: 163893

Publication Volume & Issue: Volume 10, Issue 11

Page(s): 2259 - 2266
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