DLNDD: An Explainable Deep Learning Framework for the Early Detection and Classification of Rare Diseases

Background Rare Neurological Diseases (RNDs) represent a significant global health burden, affecting over 300 million patients. The diagnostic journey for RNDs is often a prolonged process, spanning from the initial manifestation of symptoms to severe disease stages. Limited labeled clinical data and high phenotypic heterogeneity present inherent challenges for both clinicians and automated systems. The aim of this research is to develop a multi-dataset architecture utilizing advanced preprocessing and a hybrid dataset to achieve diagnostic accuracy exceeding 95% while integrating advanced interpretability techniques. Method This study utilized four publicly available datasets MRI images (A), symptom-based text (B, CSV 1), genetic data (C, CSV 2), and rare disease metadata (D, CSV 3) to support early RND diagnosis. A CNN VGG-16 architecture with Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing was employed for the MRI dataset, which consists of five severity stages. For tabular modalities, ten classifiers including MLP, SVM-RBF, Random Forest, and XGBoost were benchmarked. To ensure clinical transparency, Explainable AI (XAI) techniques, such as SHAP, LIME, and Grad-CAM, were integrated into the framework. Results The proposed DLNDD framework demonstrated ~ 100% accuracy on MRI images using the VGG-16 model. For tabular data, Logistic Regression achieved 98.89% accuracy on symptoms, Random Forest reached 96.67% on genetics, and XGBoost achieved 84.47% accuracy on Orphanet metadata. Conclusions The novel Deep Learning based Neurological Diseases Detection (DLNDD) illustrated that modality-specific, XAI achieved clinical meaningful findings even under rare diseases data constraints. The DLNDD outperformed previous studies in terms of accuracy and clinical interpretability. It also provides a replicable footprint for multimodal rare disease autonomous systems and points toward federated, fusion-ready architectures.