Computer-Aided Diagnosis using Class-Weighted Deep Neural Network

Pritam Sarkar, Vandad Davoodnia, Ali Etemad

Abstract: Computer-aided diagnosis has become a major focal point of Artificial Intelligence. Interpreting medical images is often time-consuming and requires significant human expertise. Hence, there is an increasing demand to use machine learning techniques to correctly classify different medical images captured by mammography, CT scans, and MRI among others. This paper presents a deep learning method for computer-aided differential diagnosis of benign and malignant breast cancer tumors by avoiding potential errors caused by poor feature selection as well as class imbalances in the dataset. We design, develop and test an end-to-end convolutional neural network architecture for two different breast cancer datasets of fine needle aspiration biopsy samples, and show that our network outperforms the state of the art. Furthermore, we have introduced a loss coefficient which can be adjusted to fine-tune the performance of our network. The proposed method can be used to support oncologists in the detection of breast cancer with high confidence.