Brain Tumor Detection from MRI Using Deep Learning

Brain tumors represent one of the most critical neurological disorders, where early and accurate diagnosis is essential for improving patient survival and treatment outcomes. Magnetic Resonance Imaging (MRI) remains the preferred modality for detection due to its high resolution and non-invasive nature. However, manual interpretation of MRI scans is time-intensive, requires specialized expertise, and is prone to variability, particularly when tumor boundaries are indistinct. This study proposes an automated brain tumor detection and segmentation system leveraging deep learning techniques. The framework integrates EfficientNet for tumor classification and U-Net++ for pixel-level segmentation. MRI images undergo preprocessing steps—resizing, normalization, and noise reduction—to ensure consistency and enhance quality. EfficientNet then extracts high-level features to classify scans into tumor and non-tumor categories with high accuracy. Upon detection, UNet++ performs precise segmentation, utilizing its nested encoder–decoder architecture and dense skip connections to achieve improved boundary delineation. The system is implemented using Python-based deep learning frameworks, enabling efficient model integration and rapid processing. By reducing reliance on manual interpretation, the proposed solution enhances diagnostic accuracy, minimizes analysis time, and supports early-stage tumor detection. This work demonstrates the practical application of artificial intelligence in medical imaging, offering reliable diagnostic support for radiologists and healthcare institutions.