Developing a Convolutional Neural Model for Segmentation of Medical Images

Medical image segmentation is a new biomedical image processing method that has made a significant contribution to sustainable health care. It has now become a major study area in the realm of computer vision research. Medical image processing based on deep convolutional neural networks has become a research hotspot due to the fast development of deep learning techniques. An additional cascade component is included to the architecture in this article to better replicate spatial closure tags' direct dependences. It is also utilised for post-segmentation processing, resolving the conflict between classification performance and network intensity and number of mixing times in a typical convolutional net. Numerous state-of-the-art deep recognition models, and a multi-atlas segmentation methodology, are outperformed by our methodology.