Publication

Abstract : According to WHO, the most dangerous disease prevailing among women is breast cancer. It is among one of the diseases that is untraceable in the beginning. About 1 in 8 women suffer breast cancer and even results in the removal of their breast. In this domain, a novel experiment to classify breast cancer using convolutional neural network and fuzzy system is introduced. A combination of convolution neural network and fuzzy system has been devised for grouping similar masses of benign and malignant in mammography database based on the mass area in breast. The mammography images are taken for image enhancement and image segmentation for identifying the mass area and the classic neural network architecture (Alexnet) performs the feature extraction. After that it is followed by fuzzy system for finding how much denser the malignant or benign cancer is. A well-known classic neural network architecture AlexNet is employed and is fine tuned to group similar classes. The fully connected (fc) layer is replaced with support vector machine (SVM) to improve the classification effectiveness. The results are derived using the following publicly available datasets: (1) digital database for screening mammography (DDSM), (2) curated breast imaging subset of DDSM (CBIS-DDSM) and (3) mammography image analysis society (MIAS). Data augmentation is also performed to increase the training samples and to achieve better accuracy.