Enhanced Deep Learning Approaches For Brain Tumor Diagnosis

Determining whether the brain contains a tumor in its initial stages leads to effective medical intervention for boosting patient survival. This study explores a new technique of the classification of brain tumors involving a combination of Convolutional Neural Networks and Support Vector Machines, where the system makes differential diagnosis using MRI scans against three types of tumors; Glioma, Meningioma, and Pituitary. To provide accurate analysis based on patterns, CNNs for feature extraction and SVM to classify images are used. Since the preprocessing techniques used ensure perfection in accuracy, partial differential equations are used for enhanced image enhancement and Gray-Level Co-occurrence Matrix, thereby contributing to texture analysis. Building the model with Tensor Flow on GoogleNet architecture optimized towards high performance is considered optimal. The evaluation results show that the system works well, and the system is able to achieve a high accuracy rate on the medical datasets. The proposed framework offers a reliable and efficient tool for assisting medical professionals in early diagnosis, thereby contributing to better treatment planning.