Brain tumors are a group of tissues formed by the continuous collection of irregular cells, and it is important to classify brain tumors for magnetic resonance imaging (MRI) treatment. Human examination is the standard for MRI detection of brain tumors and tumor tissue. Visualized image organizes the magnetic resonance image of the brain and provides various mechanisms depending on the body tissue. This article describes another classification of brain tumors using spider hybrid optimization and deep neural networks (SONN). Classification involves three steps. First, the scalp is pretreated with a special cleanser. In the second stage of the tumor, FCM centrifugation optimization (FCM-CO) and modified growth optimization (MRGO) are used to isolate the tumor area from the MR image. It integrates the selected tumor's features using the SONN algorithm's features. Specific classifications of brain tumors are evaluated based on their own data, and the results show solid performance that differs from current methods. The display of the specified classifier is different from the current latest classification.
