Multi-Class Breast Cancer Classification using Deep Learning Convolutional Neural Network

• Published in: International journal of advanced computer science & applications Vol. 9; no. 6
• Main Authors: Nawaz, Majid, A., Adel, Hassan, Taysir
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2018
• Subjects: Artificial neural networks; Breast cancer; Classification; Deep learning; Diagnosis; Image processing; Machine learning; Mammography; Medical imaging; Neural networks; Software; Tumors
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2018.090645

Breast cancer continues to be among the leading causes of death for women and much effort has been expended in the form of screening programs for prevention. Given the exponential growth in the number of mammograms collected by these programs, computer-assisted diagnosis has become a necessity. Computer-assisted detection techniques developed to date to improve diagnosis without multiple systematic readings have not resulted in a significant improvement in performance measures. In this context, the use of automatic image processing techniques resulting from deep learning represents a promising avenue for assisting in the diagnosis of breast cancer. In this paper, we present a deep learning approach based on a Convolutional Neural Network (CNN) model for multi-class breast cancer classification. The proposed approach aims to classify the breast tumors in non-just benign or malignant but we predict the subclass of the tumors like Fibroadenoma, Lobular carcinoma, etc. Experimental results on histopathological images using the BreakHis dataset show that the DenseNet CNN model achieved high processing performances with 95.4% of accuracy in the multi-class breast cancer classification task when compared with state-of-the-art models.