CNN Framework for Automatic Segmentation of Breast Section from Thermal Images

Breast cancer is considered a severe illness in the female society, and if left untreated, it can be fatal. It is always desirable to detect the BC early utilizing a selected imaging strategy. Thermogram supported breast abnormality detection is one of the recent technique and this gives the necessary information in the form of the distributed thermal pattern. This research aims to implement the Convolutional-Neural-Network (CNN) based segmentation technique to extract breast region from the chosen thermogram. This scheme's multiple stages include: (i) data collecting and processing, (ii) implementation of CNN segmentation to extract the breast, (iii) comparing it to the binary-mask and computing performance metrics, and (iv) performance evaluation and verification of the chosen CNN techniques. Pre-trained CNN segmentations are used in this work to extract the necessary section from the thermogram, and the experimental results show that the VGG-UNet methodology helps to extract the essential region with an enhanced accuracy of 97.260.64% when compared to other CNN approaches.