Analysis of Deep Learning Models to Detect Breast Cancer from Histopathology Images
Anjali Gautam, Satish Kumar Singh · 2023
Breast cancer is a disease where breast cells grow out of control and leads to cancer. Various methodologies have been developed to identify breast cancer. In this paper, we have developed an approach to classify breast cancer from histopathology images. The approach makes use of deep learning based architectures by setting same parameters for all while training and testing on them. Thereafter, all the architectures are compared to see which one is most suited for the classification of breast cancer. Previous works on AlexNet, VGG, ResNet have already been published, and here we have tried to see the performance of those models which have less number of trainable parameters, namely DenseNet121, DenseNet169, DenseNet201, EfficientNetB0, EfficientNetB5, EfficientNetV2B0 and EfficientNetV2S. Here, all the experiments are conducted on BreakHis histopathology dataset by utilizing all the images of resolutions 40X, 100X, 200X and 400X of benign and malignant cancer.