ROISegNet: A Deep Learning Framework for Automatic Segmentation of Region of Interest from Breast Thermogram Imagery
Keywords:
Deep Learning, Region of Interest Segmentation, Breast Cancer, Atrous Convolution, Sematic Image SegmentationAbstract
Among the top causes of mortality for women worldwide is breast cancer. With the emergence of non-invasive imaging technology known as breast thermography, it is possible to know abnormalities in breast that may lead to breast cancer over a period of time. Therefore, early detection of breast cancer probability could save lives and get rid of such cancer altogether. At the same time, Artificial Intelligence (AI) has become a technology innovation for solving problems in healthcare domain. In this context, it is indispensable to exploit AI enabled methods such as deep learning and breast thermography for early detection of breast cancer. However, the research is this paper is confined to breast Region of Interest (ROI) segmentation using deep learning to facilitate mechanisms for breast cancer screening later. Existing deep learning model named atrous convolution is found suitable for semantic segmentation. Our deep learning system, ROISegNet, was suggested for automated segmentation of ROI from breast thermogram imagery. Our framework uses atrous convolution as part of encoder and designs decoder module for leveraging efficiency in semantic segmentation. We proposed an algorithm named Learning based Breast ROI Segmentation (LbBROIS) to realize our framework. Our empirical study made with the benchmark dataset DMR-IR revealed with, ROISegNet outperforms existing models such as VGG19, ResNet50, InceptionV3 and Atrous Convolution with highest accuracy 98.63%.
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