Intelligent Neurodiagnostic Platform for Brain Tumor and Alzheimer's Detection Using Deep Learning Models

The timely and precise identification of neurological conditions such as brain tumors and Alzheimer’s disease carries profound implications for patient survival, treatment efficacy, and long-term quality of life. This paper introduces NeuroDetect AI, a web-deployable Intelligent Neurodiagnostic Platform that automates MRI-based brain scan classification across brain-tumor-positive, Alzheimer’s-positive, and neurologically normal categories. The system adopts a dual deep learning strategy: a custom Modified Convolutional Neural Network for brain tumor classification and an EfficientNetB0 transfer-learning model for Alzheimer’s detection. A standardized preprocessing pipeline consisting of grayscale conversion, CLAHE, intensity normalization, and augmentation feeds both models. The platform uses a Flask REST API for inference and real-time doctor-patient communication, while a Django-backed module manages authentication, patient records, appointment scheduling, and role-based access control. Evaluation on 6,500 combined MRI scans from the Kaggle Brain Tumor MRI Dataset and ADNI yielded 96.9% accuracy for tumor detection and 95.8% for Alzheimer’s staging, with an average inference latency of 1.3 seconds per scan. The platform integrates confidence-based clinical triage, physician override, PDF report generation, and real-time consultation within a single deployable web application.