Authors: Noyal Biju, Dharunkumar C, Aziz Pardiwala, Abhishek Pillai
Abstract: Accurate and timely detection of brain tumors is a critical challenge in medical imaging, directly influencing treatment planning and patient prognosis. Conventional diagnostic approaches based on manual interpretation of Magnetic Resonance Imaging (MRI) scans are often limited by subjectivity, inter-observer variability, and increasing workload on radiologists. This study presents a robust deep learning–driven framework for automated brain tumor detection and classification, leveraging advanced Convolutional Neural Network (CNN) architectures. The proposed model employs a transfer learning approach using a pre-trained VGG16 network, fine-tuned on a curated dataset of MRI images to capture domain-specific features. A comprehensive preprocessing pipeline—including image normalization, resizing, denoising, and intensity standardization—is integrated with data augmentation techniques to address class imbalance and enhance generalization. The architecture incorporates fully connected layers with dropout regularization to mitigate overfitting and improve model stability Model performance is rigorously evaluated using standard metrics such as accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (ROC-AUC). Experimental results demonstrate high classification performance, indicating the model’s capability to effectively distinguish between tumor and non-tumor cases. Furthermore, comparative analysis with baseline models highlights the superiority of the proposed approach in terms of feature extraction efficiency and predictive accuracy. The system offers significant potential for real-world clinical integration by reducing diagnostic latency, minimizing human error, and providing decision support for radiologists. This research underscores the transformative role of deep learning in medical image analysis and establishes a scalable foundation for future advancements, including multi-class tumor classification and explainable AI-driven diagnostics.
