Histopathological Image Analysis Using Deep Learning Framework

Breast cancer has the highest morbidity and fatality rates of all cancers. Early identification of this is critical step in histopathological image analysis (HIA). Manual methods are will take much time and also having of lot errors in pathologists' competence. Current HIA, ignoring on histopathology image segmentation of breast cancer (BC) because to its complicated features and unavailability of previous data with detailed annotations. Our approach uses only graph-based segmentation to classify breast cancer histopathology images. Graph based segmentation images extract efficient features. Then, using recursive feature elimination (RFE), images of breast cancer are categorized. The solution provided here tackles large-scale image processing in breast cancer histopathology images. The suggested technique accurately classifies breast histopathology pictures as abnormal or normal, supporting early breast cancer diagnosis.