Breast Mass Detection and Classification Using Deep Convolutional Neural Networks for Radiologist Diagnosis Assistance

Several developments in computational image processing methods assist the radiologist in detecting abnormal breast tissue in recent years. Consequently, deep learning-based models have become crucial for early screening and interpretation of mammographic images for breast masses diagnosis, helping for successful treatment. Breast masses and calcification is an essential parameter for the prognosis of breast cancer. However, the mammographic image's mass detection needs a deeper investigation due to the breast masses' heterogeneity and anomalies' characteristics that are easily confused with other objects present in the image. Hence, this study proposed a deep learning-based convolutional neural network (ConvNet) that will incorporate both mammography and clinical variables to predict and classify breast masses to assist the expert's decision-making processes. We trained our proposed model with 322 scanned digital mammographic images of the MIAS (Mammogram Image Analysis Society) dataset and 580 images of the private dataset to evaluate the performance, which is highly imbalanced. This study aimed to perform an automatic and comprehensive characterization of breast masses using appropriate layers deep ConvNet model with high accuracy true-positive rate, decreased error rate and applying data-augmentation techniques. We obtained a classification accuracy of 97% applying the filtered deep features, which is the best performance from the existing approaches.