SAMPLE ENHANCING BRAIN DIASEASE DETECTION: A DEEP LEARNING APPROCH TO MEDICAL IMAGE CLASSIFICATION AND SEGMENTATION

Abstract

This research aims to augment diagnostic precision and efficiency in neurology by developing an automated deep learning system to classify brain anomalies from MRI scans, assisting physicians in early detection and disease management. The system classifies brain MRI images into six distinct categories: normal brain, hemorrhage, Alzheimer’s disease, glioma, meningioma, and pituitary tumor. The methodology leverages convolutional neural networks (CNNs), a proven approach for medical imaging analysis. Four specific CNN models were meticulously developed, trained, and evaluated. Promising results indicate strong potential for clinical application. Based on comprehensive performance metrics, the fourth model demonstrated highly satisfactory outcomes, achieving 94% accuracy, 85% precision, and 82% recall. Furthermore, it attained a 98% F1-score with a loss function of 10%. Validation via a confusion matrix confirmed the model’s robust predictive capability, accurately classifying all test images in the evaluation. The research successfully implements a viable CNN-based classifier for multi-category brain disease identification. The system's high-performance metrics underscore its potential as a supportive diagnostic aid, promising to enhance the speed and accuracy of neurological assessments in clinical settings, notably for early diagnostics at facilities like the Lumbu-Lumbu Hospital Center. Further research should involve validation on larger, multi-center datasets and integration into clinical workflow for real-time assessment.