An efficient framework for brain cancer identification using deep learning

The fact that brain tumors belong to the most fatal illnesses, prompt and precise detection techniques are necessary. Optimization of MRI scan is the very first approach in which pre-processing and post processing are used to identify the best image for the goals of the research. Consequently, a threshold was applied to divide up the MRI pictures by incorporating the mean grey level approach. Using Hara-lick's feature equations and the spatial gray-level dependency matrix, the second stage of statistical feature analysis involved the extraction of data (SGLD). As a result, the tumor was positioned correctly and the best features were chosen. In the third step, supervised learning and artificial intelligence techniques were used to create an automated tool that could classify the photos being evaluated as having a tumor or not. An effective network performance test produced 97% of the intended outcomes.