Md. Mehedi Hassan is a Research Assistant at Texas Tech University specializing in AI-driven medical imaging, computer vision, and digital health diagnostics for metabolic disorders, fatty liver disease, and chronic health conditions.
He holds M.Sc. and B.Sc. degrees in Computer Science and Engineering and has authored peer-reviewed scientific publications (2,226+ citations, h-index 25), edited 8 scholarly books with premier publishers, and holds two granted patents. He serves as Associate Editor and Academic Editor across leading Q1/Q2 journals, and his current work bridges deep learning innovation with clinical workflow optimization to build scalable diagnostic tools.
His Field-Weighted Citation Impact (SciVal) stands at 2.09 — within Scopus's top 1% of global impact — across 47 papers as first, lead, or corresponding author.
Volumetric Segmentation
3D U-Net & hybrid CNN–Transformer architectures for liver, brain lesion, and tumor segmentation from CT/MRI, optimized for Dice similarity and low inference latency.
Metabolic & Chronic Disease AI
Machine learning pipelines for early detection of diabetes, coronary artery disease, chronic kidney disease, and obesity-related conditions from clinical and imaging data.
Biomedical Signal AI
EEG-based epileptic seizure detection and emotion recognition; brain–computer interface systems combining CNN/LSTM architectures with explainable AI.
Federated & Privacy-Preserving Learning
Federated deep learning frameworks for multi-institution medical imaging, reducing data-sharing barriers across clinical sites.
Explainable AI
Grad-CAM, LIME, and interpretability frameworks for clinical trust — applied across monkeypox, breast cancer, and NLP-based diagnostic models.
Graph Neural Networks for the Brain
GNN-based modeling of brain connectomes and networks for neurological disorder classification and personalization.