DEEP JOINT RP-NET-BASED SEGMENTATION ALGORITHM AND OPTIMIZED DEEP LEARNING FOR SEVERITY PREDICTION OF BRAIN TUMOR

Abstract

The uncontrolled growth of cells in a particular area is referred to as a tumor. The premature and precise identification of the tumor and its level have a straight impression on the patient’s survival, treatment process, and tumor progression computation. However, in the medical field, picture segmentation and classification are more important and difficult processes. Typically, the Magnetic Resonance Imaging (MRI) modality can detect malignancy. Segmenting tumor images with respect to Cerebrospinal Fluid (CSF), Grey Matter (GM), and White Matter is the most important task in MRI identification or classification (WM). The introduction of medical image analysis based on radiology pictures is a result of the significant contributions of engineering, data sciences, and medicine. The precise and automatic segmentation of tumors affords excessive support to doctors in the medicinal area, speed detection in the treatment process, computer-aided operation, radiation treatment and so on. Thus, Remora Aquila optimization (RAO)-enabled deep learning is devised for tumor classification and its severity classification. The deep learning approach is utilized for categorizing tumors as normal or abnormal as well as their severity grades. The RAO-aided deep learning system achieved improved performance with prediction error, specificity, sensitivity and testing accuracy of 0.072, 0.905, 0.925, and 0.917, respectively.