Inductive and Transfer Learning-Based Hybrid Model Techniques for Accurate and Automated Diagnosis of Neurological Diseases.

Purpose: This study presents NeuroDL, a novel deep learning-based diagnostic framework designed for the automated detection of brain tumors and Alzheimer's disease (AD) using magnetic resonance imaging (MRI). The objective is to enhance diagnostic precision and efficiency in neurology through advanced computer-aided decision support.

Methods: NeuroDL utilizes convolutional neural networks (CNNs) trained on two publicly available, annotated MRI datasets. The proposed pipeline integrates optimized preprocessing, including normalization, skull stripping, and data augmentation, followed by CNN-based feature extraction and classification. Transfer learning and fine-tuning were employed to improve generalization on limited medical data.

Results: Experimental evaluations show that NeuroDL achieves 96.8% classification accuracy for brain tumor detection and 92.4% accuracy for Alzheimer's disease diagnosis. The method also achieves an average F1-score of 0.965, precision of 0.969, and recall of 0.962 for brain tumors, and an F1-score of 0.918, precision of 0.921, and recall of 0.916 for AD. These results outperform state-of-the-art benchmarks on the same datasets.

Conclusion: Potential for real-time clinical deployment. It addresses key limitations of existing CAD systems by providing a unified, dual-disease diagnosis model with statistically validated performance gains. NeuroDL paves the way for reliable, scalable, and automated neurological disease diagnosis using deep learning.