Multi-Grade Brain Tumor Classification

Brain tumors pose a significant challenge to radiologists engaged in automated diagnosis and health monitoring due to the intricate nature of tumor grading. The literature has suggested a number of machine learning-based strategies to help radiologists in achieving more precise and dependable diagnostic assessments. This paper leverages convolutional neural networks (CNN) in conjunction with MRI detection technology to grow a particular model for the finding of brain tumor’s, facilitating the integration of computer-aided detection technologies in the medical domain. The research introduces an innovative DCNN architecture and delineates a three step preprocessing methodology designed to enhance MRI image quality and enable efficient brain tumor diagnosis. Key contributions of this work include addressing the challenge of limited datasets through the implementation of the Augmentation Technique. Advanced signal processing and image processing techniques are employed for improved feature extraction and selection. Additionally, the utilization of Multigrain offers a novel method to assess the aggressiveness of brain tumors. This research thus presents a comprehensive framework that not only enhances the correctness of brain tumor detection but also addresses data scarcity issues and incorporates state of the art systems for robust feature analysis and tumor characterization.