Deep Learning-Based Automated System for Enhanced Brain Tumor Detection and Early Diagnosis

Abstract

Brain tumors are among the most critical and life-threatening medical conditions, necessitating early and accurate diagnosis for effective treatment. This paper proposes a deep learning-based automated system for enhanced brain tumor detection and early diagnosis using MRI images. The system employs a hybrid Convolutional Neural Network (CNN) architecture integrated with transfer learning, leveraging pre-trained models like ResNet-50 to classify brain tumors into categories such as glioma, meningioma, and pituitary tumors. The model is trained and evaluated on the BraTS 2021 dataset, achieving state-of-the-art performance with an accuracy of 98.5%, precision of 97.8%, recall of 98.2%, and F1-score of 98.0%. Key contributions include the use of multi-modal MRI data, advanced data augmentation techniques, and attention mechanisms to improve feature extraction and classification accuracy. The system also addresses challenges such as class imbalance and computational complexity while ensuring interpretability through explainable AI techniques like Grad-CAM. Future work focuses on integrating additional imaging modalities, optimizing for real-time edge deployment, and exploring federated learning for privacy-preserving collaborative training. This research demonstrates the potential of deep learning to revolutionize brain tumor diagnosis, offering a robust, accurate, and scalable solution for early detection and improved patient outcomes.