Deep Learning Based Classification Approach to Improve Breast Cancer Screening Using Histopathological Images

This Breast cancer is a prevalent disease and is the leading cause of death in women The detrimental impact of the disease can be effectively managed when diagnosed in initial stages, increasing the efficacy for the plethora of treatments administered. The current piece of research envisages the precise means of diagnosis based on computational technique ResNet-50 is a convolutional neural network for providing the categorisation of histopathological microscopy images. The suggested model utilises translocation learning technique ResNet-50 CNN associated with the ImageNet to train and bifurcate Break His dataset for the two conditions: benign and malignant. This simulation presented that the suggested model acquires an extraordinary bifurcation of 99% efficiency exceedingly better than the wide other models trained for the BreakHis dataset.