Mass Detection in Breast Using Transfer Learning for Computer Aided Diagnosis

Abstract

Mammography is the most widely used gold standard method for the screening of the breast cancer and Mass detection is the prominent pre-processing step. State-of-art performances of the DCNN architectures in the field of classification made them an obvious choice for the image classifications. However, due to limited availability of medical data, application of DCNN architectures in medical images is challenging. In this project our contribution was mass detection in mammographic images using two different approaches. First approach was applying transfer learning concept which lessen the demand of data to use a pre-trained publicly available VGG16 model and second approach was training a Alexnet from the scratch to classify mass and non-mass mammographic images. Both the models were trained and tested with different hyperparameters and different data size. From the experiment results, it can be concluded that transfer learning approach for VGG16, training from fully connected layer 2 (fc2) has promising expected result for mass detection. In our research, maximum accuracy for mass detection was 93.60% for VGG16 with 13500 train images.