Automatic Edge Detection Model of MR Images Based on Deep Learning Approach

In medical imaging automatic computer-based diagnosis is taken into account because the most difficult issues within the area of medical image process. Pattern recognition, machine learning, and deep learning approaches primarily influence the problem of automatically finding a decision to detect the fine edges of medical images. Here, deep learning technique is proposed to seek out fine edges automatically of the medical MR images corrupted by noises. Detection based on deep learning approach in medical image processing is employed to detect certain structures or organs and delimit regions of interest from the medical images. The performance of the method proposed in this chapter is evaluated with metrics like peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and edge keeping index (EKI). Experimental results show the approach proposed in this research work that exhibits far better performance than the prevailing techniques as well as generalized soft-computing approaches.