Design and Implementation of Transfer Learned Deep CNN with Feature Fusion for Automated Mammogram Classification

Radiologists frequently struggle to define mammography mass lesions, resulting in unneeded breast biopsies to eliminate suspicions, which adds exorbitant costs to an already overburdened patient and medical system.Existing models have limited capability for feature extraction and representation, as well as cancer classification. Therefore, we built deep Convolution neural networks based Computer-aided Diagnosis system to assist radiologists in classifying mammography mass lesions. Here, Two-view LASSO regression feature fusion and fine-tuned transfer learning network model VGG16 were applied for identification of mammogram cancer. First, two independent CNN branches are utilized to extract mammography characteristics from two different perspectives. Feature Extraction is performed by fine-tuning pre-trained deep network models VGG16 which extracts deep convolutional features. Second, the features of the VGG16 models are serially fused using LASSO regression. Lastly, the fused features are entered into the Fully Connected Layer for mammogram classification. The high accuracy of 95.24, senstitivity of 96.11% and AUC score of 97.95% of the proposed approach revealed that it should be used to enhance clinical decision-making.