Convolutional neural network improvement for breast cancer classification. (15th April 2019) - Ting, Fung Fung; Tan, Yen Jun; Sim, Kok Swee
Highlights: Propose a deep classification algorithm for mammogram images. The deep classification performance is improved by the feature wise pre-processing. Application of proposed technique to detect and classify breast cancer. An intricate designed classification trained using extracted features. Achieved classification accuracy of 90.50% and specificity of 90.71%. Abstract: Traditionally, physicians need to manually delineate the suspected breast cancer area. Numerous studies have mentioned that manual segmentation takes time, and depends on the machine and the operator. The algorithm called Convolutional Neural Network Improvement for Breast Cancer Classification (CNNI-BCC) is presented to assist medical experts in breast cancer diagnosis in timely manner. The CNNI-BCC uses a convolutional neural network that improves the breast cancer lesion classification in order to help experts for breast cancer diagnosis. CNNI-BCC can classify incoming breast cancer medical images into malignant, benign, and healthy patients. The application of present algorithm can assist in classification of mammographic medical images into benign patient, malignant patient and healthy patient without prior information of the presence of a cancerous lesion. The presented method aims to help medical experts for the classification of breast cancer lesion through the implementation of convolutional neural network for the classification of breast cancer. CNNI-BCC can categorize incoming medical images as malignant, benign or normal patient with sensitivity, accuracy, area under the receiver operating characteristic curve (AUC) and specificity of 89.47%, 90.50%, 0.901 ± 0.0314 and 90.71% respectively.