Next-Generation Neurodiagnostics: Deep Learning, Hyperspectral Imaging, and Computing Acceleration in Brain Condition Detection

Dear Colleagues,

Current promising developments in artificial intelligence, advanced imaging modalities, and high-performance computing are opening new frontiers in the early diagnosis, monitoring and understanding of brain disorders. Efficient computational acceleration techniques can enable real-time response and fast learning cycles; combined with hyperspectral imaging (HSI) and advanced ML/DL algorithms, they are driving the shift toward non-invasive, accurate, and scalable neurodiagnostic tools.

This Special Issue, “Next-Generation Neurodiagnostics: Deep Learning, Hyperspectral Imaging, and Computing Acceleration in Brain Condition Detection”, aims to garner original research and comprehensive reviews that focus on novel methodological advances and translational applications in this evolving field. We welcome both specific and interdisciplinary contributions that utilize machine learning, spectral data analysis, and neuroimaging technologies to enhance our ability to diagnose and characterize neurological conditions at multiple spatial, temporal, and spectral scales.

Topics of interest for this Special Issue include, but are not limited to, the following:

1. Hyperspectral imaging for brain tissue analysis and disease detection;
2. Deep learning methods for spectral and multimodal neuroimaging data;
3. Multimodal data fusion combining HSI, MRI, fMRI or PET;
4. Microscopy imaging;
5. Real-time brain imaging through computing acceleration (e.g., GPUs, FPGA, edge computing);
6. Spectral analysis and biomarkers for neurological conditions diagnosis;
7. Image segmentation, classification, and anomaly detection in neural datasets.

Dr. Miguel Chavarrías
Dr. César Sanz
Guest Editors

Dr. Jaime Sancho
Guest Editor Assistant

Manuscript Submission Information

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• hyperspectral imaging
• multimodal imaging
• neurodiagnostics
• deep learning
• machine learning
• accelerated computing