Efficient segmentation model using MRI images and deep learning Techniques for Multiple Sclerosis Classification

Abstract

The segmentation models employing deep learning offer successful outcomes over multiple medical image complex data resources and public data resources important for huge pathologies. During the identification of multiple sclerosis, the observation of entire tumors from the Magnetic Resonance Imaging (MRI) sequence is complex. Furthermore, it is necessary to identify the small tumors from the pictures in the prognosis phase to offer good treatment. The deep learning-assisted identification models solve the issue of the imbalance data and the false positive results are more in the conventional models. Besides, these methodologies offer a good tradeoff between the precision measure and recall measure. Thus, the latest deep learning-assisted MRI image segmentation and categorization model is developed to detect multiple sclerosis at the initial stage. Here, the MRI pictures are initially gathered from traditional online databases. The gathered images are directly given to the image segmentation process, where the Multi-scale Adaptive TransResunet++ (MSAT) is adopted to perform the lesion segmentation appropriately. The attributes present in the MSAT are optimized with the support of the developed Random Opposition of Cicada Swarm Optimization (ROCSO). Then, the segmented pictures are subjected to the categorization process, where the Hybrid and Dilated Convolution-based Adaptive Residual Attention Network (HDCARAN) is utilized to categorize the lesions from the MRI images very effectively to detect the multiple sclerosis of patients. Here, the attributes present within the HDCARAN are tuned via the same ROCSO. The implementation results are analyzed through the previously dev