📅 13 October 2025
DOI: 10.62411/jcta.14661
Transformer-Augmented Deep Learning Ensemble for Multi-Modal Neuroimaging-Based Diagnosis of Amyotrophic Lateral Sclerosis
Journal of Computing Theories and Applications
Universitas Dian Nuswantoro
📄 Abstract
Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disorder that presents significant diagnostic challenges due to its heterogeneous clinical manifestations and symptom overlap with other neurological conditions. Early and accurate diagnosis is critical for initiating timely interventions and improving patient outcomes. Traditional diagnostic approaches rely heavily on clinical expertise and manual interpretation of neuroimaging data, such as structural MRI, Diffusion Tensor Imaging (DTI), and functional MRI (fMRI), which are inherently time-consuming and prone to interobserver variability. Recent advances in Artificial Intelligence (AI) and Deep Learning (DL) have demonstrated potential for automating neuroimaging analysis, yet existing models often suffer from limited generalizability across modalities and datasets. To address these limitations, we propose a Transformer-augmented deep learning ensemble framework for automated ALS diagnosis using multi-modal neuroimaging data. The proposed architecture integrates Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Vision Transformers (ViTs) to leverage the complementary strengths of spatial, temporal, and global contextual feature representations. An adaptive weighting-based fusion mechanism dynamically integrates modality-specific outputs, enhancing the robustness and reliability of the final diagnosis. Comprehensive preprocessing steps, including intensity normalization, motion correction, and modality-specific data augmentation, are employed to ensure cross-modality consistency. Evaluation using 5-fold cross-validation on a curated multi-modal ALS neuroimaging dataset demon-strates the superior performance of the proposed model, achieving a mean classification accuracy of 94.5% ± 0.7%, precision of 93.9% ± 0.8%, recall of 92.9% ± 0.9%, F1-score of 93.4% ± 0.7%, spec-ificity of 97.4% ± 0.6%, and AUC-ROC of 0.968 ± 0.004. These results significantly outperform baseline CNN models and highlight the potential of transformer-augmented ensembles in complex neurodiagnostic applications. This framework offers a promising tool for clinicians, supporting early and precise ALS detection and enabling more personalized and effective patient management strategies.
🔖 Keywords
#Amyotrophic lateral sclerosis; Deep learning; Disease classification; Feature Fusion; Medical image analysis; Multimodal Diagnosis; Neurodegenerative diseases; Vision transformer
ℹ️ Informasi Publikasi
Tanggal Publikasi
13 October 2025
Volume / Nomor / Tahun
Volume 3, Nomor 2, Tahun 2025
📝 HOW TO CITE
Asuai, Clive; Andrew, Mayor; Arinomor, Ayigbe Prince; Ogheneochuko, Daniel Ezekiel; Joseph-Brown, Aghoghovia Agajere; Merit, Ighere; Collins, Atumah, "Transformer-Augmented Deep Learning Ensemble for Multi-Modal Neuroimaging-Based Diagnosis of Amyotrophic Lateral Sclerosis," Journal of Computing Theories and Applications, vol. 3, no. 2, Oct. 2025.
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