Augmented deep learning architecture to effectively segment the cancerous regions in biomedical images

Abstract

The segmentation of cancerous region is most crucial task in biomedical image processing. The detection of cancerous tissues in early stage is essential for planning the treatment of cancer. In this manuscript, we proposes a modified deep learning architecture for efficient segmentation of biomedical images. Our proposed network effectively Figure out the boundaries of the cancerous region. The network once trained produced exceedingly good results on other datasets without retraining. The evaluation metrics mIoU and BF score shows an improvement of 2.5% and 3% for brain tumour segmentation when trained network is tested on other dataset. Improvement of 2% in both the metrics was achieved for skin lesion segmentation when trained network was tested on other dataset for 100 epochs of training. © 2020 IEEE.