Deep CNN-Based MRI Imaging for Brain Tumor Detection and Classification
Prachi V. Kale, Ajay B. Gadicha
Abstract
Depression and anxiety disorders are two psychological problems that can be significantly triggered by brain tumors. Detecting various disorders requires patients to receive assistance from healthcare image analytics. Accurate tumor identification facilitates the process of recovering from a brain tumor. Classifying the types of brain tumors is a crucial factor that relies on the doctor's competence and understanding. To assist doctors, a sophisticated method for identifying and categorizing brain tumors is required. This work is novel as it employs a deep CNN approach to classify various types of brain tumors. For a speedy and effective recovery, analysis and tumor categorization are crucial, and MRI brain image analytics via a Deep CNN is producing exceptional results in this area. The Deep CNN classifies the various tumor types and trains the data accordingly. The classification of brain tumors into four distinct primary subtypes, namely Glioma, Meningioma, Pituitary, and Healthy, has been achieved through a proposed approach. The proposed model outperforms the most recent approaches in Brain tumors were classified and categorized with an accuracy rate of 97.85%. This suggested solution holds the potential to provide valuable clinical aid to the healthcare industry.