Breast Masses Segmentation: A Framework of Skip Dilated Semantic Network and Machine Learning

Abstract

Many medical specialists used Computer Aided Diagnostic (CAD) systems as a second opinion to detect breast masses. The poor visualization of mass images makes it difficult to identify precisely. To segment the lesions from the mammograms is a difficult task due to different shapes, sizes, and locations of the masses. The motivation of this study is to develop a method that can segment breast mass lesions from mammogram images. The objective is to perform the segmentation of the breast mass mammogram images more precisely at an early stage. Breast mass segmentation is always a basic requirement in computer-aided diagnosis systems. In this study segmentation of the masses abnormalities from the mammogram images is performed by using the Skipping Dilated semantic segmentation approach. The study uses class weights and Dilation factor using semantic Convolutional Neural Network (CNN). It overcomes the class misbalance in tumors and background class, that affect the mean Intersection over Union (MIOU), and weighted-IOU (WIOU) by using class weights. Secondly, dilation convolution magnifies the receptive field exposure that enriches the convolutional operation with context attentiveness. Two public datasets of mammography INbreast and CBIS-DDSM are used. The WIOU of Skipping Dilated Semantic CNN for INbreast is 98.51% and CBIS-DDSM is 94.82% achieved.