DEEP LEARNING PARADIGMS FOR PRECISE BRAIN TUMOUR CATEGORIZATION

This paper presents a deep learning-based approach Brain tumors are a major threat to human life due to their variability and complexity. Early and correct classification of brain tumors is extremely crucial for the selection of proper treatment protocols and enhancing the survival ratio of patients. Deep learning dominated the field of medical imaging in recent years and provided automatic, high-accuracy tumor detection and classification solutions. This research investigates various deep learning paradigms, such as Convolutional Neural Networks (CNNs), Transfer Learning, and Hybrid Architectures, to improve the accuracy of brain tumor classification from MRI images. The suggested method is tested with benchmark datasets, and its performance is evaluated for various models in terms of accuracy, sensitivity, and specificity. Experimental results prove the effectiveness of deep learning methods for extracting complex patterns in brain images over traditional machine learning methods. This study highlights the importance of intelligent diagnostic systems in radiologists' assistance and computer-aided medical diagnosis Brain tumor classification is essential for early diagnosis and efficient planning of treatment. This study investigates deep learning methods, such as CNNs and transfer learning, for precise tumor classification from MRI images. The models are trained on benchmark datasets for the detection and classification of tumors with high accuracy. Comparative analysis shows enhanced performance of deep learning over traditional methods. The results highlight the potential of AI-based solutions to improve medical diagnostics These deep learning paradigms not only shorten the diagnostic process but also support radiologists in decision-making with higher speed and reliability. Future work will concentrate on implementing these models in real-time clinical workflows for extensive healthcare benefits. Keywords: Brain Tumor, Deep Learning, Classification, Medical Imaging, Disease Diagnosis