Computer-aided mammogram diagnosis system using deep learning convolutional fully complex-valued relaxation neural network classifier - Duraisamy, Saraswathi; Emperumal, Srinivasan (IET computer vision, 2017)
In this study, a novel deep learning-based framework for classifying the digital mammograms is introduced. The development of this methodology is based on deep learning strategies that model the presence of the tumour tissues with level sets. It is difficult to robustly segment mammogram image due to low contrast between normal and lesion tissues. Therefore, Chan-Vese level set method is used to extract the initial contour of mammograms and deep learning convolutional neural network (DL-CNN) algorithm is used to learn the features of mammary-specific mass and microcalcification clusters. To increase the classification accuracy and reduce the false positives, a well-known fully complex-valued relaxation network classifier is used in the last stage of DL-CNN network. Experimental results using the standard benchmarking breast cancer dataset (MIAS and BCDR) show that the proposed method exhibits significant improvement in performance over the traditional methods. Performance measures such as accuracy, sensitivity, specificity, AUC achieved are 99%, 0.9875, 1.0 and 0.9815, respectively. The proposed framework performs well in classifying the digital mammograms as normal, benign or malignant and its subclasses as well.