Brain Tumour Detection and Multi‐Classification Using Advanced Deep Learning Techniques

Abstract

The early detection and accurate classification of brain tumors are pivotal in enhancing treatment efficacy and patient survival rates. Traditional diagnostic methods, while effective to a degree, are often invasive and reliant on subjective interpretations. This paper introduces a novel approach using advanced deep learning techniques to automate the detection and multi-classification of brain tumors from medical imaging data. Leveraging a comprehensive dataset, we employed state-of-the-art convolutional neural networks (CNNs), incorporating innovative mechanisms such as transfer learning and attention models to refine the accuracy and interpretability of tumor identification and classification. Our methodology encompasses rigorous preprocessing, data augmentation, and a multi-faceted evaluation framework to assess model performance comprehensively. The results indicate a significant improvement over conventional methods and existing machine learning models, showcasing high precision, recall, and F1 scores across multiple tumor types. This research not only contributes to the body of knowledge in medical image analysis but also presents practical implications for integrating advanced AI technologies into clinical diagnostics, thereby potentially transforming patient outcomes through earlier and more accurate diagnoses. The discussion extends to the challenges faced, including dataset imbalances and model deployment in healthcare settings, and proposes directions for future research to further enhance model effectiveness and applicability.