Novel Deep CNN Model based Breast Cancer Classification

Breast cancer is one of the terrible diseases among women worldwide. Better classification of breast cancer saves more human life and gives more confidence to extent their survivability. Deep Learning plays a vital role to solve real world complex problems in almost all domains including medical image processing. In this study, a novel DeepCNN model is proposed to classify Breast Cancer with better accuracy. Further, hyper-parameter optimization using Random Search is implemented to optimize the number of epochs, learning rate, and a dropout rate of the proposed DeepCNN model. Finally, the results are compared with various pre-trained models such as VGG19, Resnet50, Resnet101V2, InceptionResNetV2, InceptionV3, and Xception. Among all, the novel custom DeepCNN model produces higher accuracy of 99.18% using Random Search optimizer.