Breast Cancer Classification using CNN with Transfer Learning Models

Breast cancer is the deadliest and most common cancer in the world. Early treatment of this cancer can help to nip it in the bud. In present medical setting, this cancer is identified by manual clinical procedures, which can lead to human errors and further delay the treatment procedure. So, we propose a Convolutional Neural Network (CNN) model employed with transfer learning approach with RESNET50, VGG19 and InceptionV3 algorithms. The histopathological image dataset is used to detect cancer cells in the tissues of the breast. We examine the performance of different models based on their accuracy, by varying different optimizers (Adam, SGDM and RMSProp) for each transfer learning model. The results show that the Inception-V3 model with Adam optimizer outperforms VGG19 and RESNET-50 in terms of accuracy.