An optimized framework for brain tumor detection and classification using deep learning algorithms

Brain tumors are among the most critical and life-threatening diseases, requiring early and accurate diagnosis for effective treatment. Traditional diagnostic methods rely on manual assessment of medical images, which can be time-consuming and prone to human error. This study presents an automated approach for brain tumor detection and classification using deep learning and texture analysis techniques. A convolutional neural network (CNN) is employed for feature extraction and classification, while texture analysis methods, such as Gray Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP), enhance the model's ability to distinguish tumor types. The proposed framework is trained on MRI datasets and achieves high accuracy in detecting and classifying brain tumors into categories such as glioma, meningioma, and pituitary tumors. The integration of deep learning with texture-based feature extraction improves robustness and interpretability, making it a promising tool for assisting radiologists in clinical decision-making. Experimental results demonstrate the efficiency of the model in achieving superior classification performance compared to conventional machine learning approaches. The aim of the project is to achieve higher accuracy and reliability for real world MRI data using AI and ML domain knowledge.