2026
Cervical Intraepithelial Neoplasia (CIN1-3) Disease Grading Using a Mixture of Experts Approach
Journal of Imaging Informatics in Medicine
Pioneering AI Solutions in Healthcare & Medicine
Biomedical Engineer & AI Researcher pushing the boundaries of healthcare technology
Dr. Abdulkader Helwan is a Lebanese biomedical engineer and artificial intelligence researcher whose work bridges healthcare, computational intelligence, and deep learning. He holds a PhD in Biomedical Engineering from Near East University, where his doctoral research focused on fuzzy neural networks for breast cancer identification using medical imaging.
Over the past decade, Dr. Helwan has contributed to academia and industry through roles at institutions such as Linköping University (Sweden), Lebanese American University, and the University of Malta, as well as collaborations with Degen Medical (USA). His projects span medical image analysis, computational neuroscience, radionuclide identification, and multimodal AI models for disease diagnosis and grading.
He has authored numerous peer-reviewed publications in journals including Physiological Measurement, Diagnostics, and the Journal of Personalized Medicine, and has presented at international conferences on biomedical engineering, machine learning, and healthcare AI.
Member of the Order of Engineers (Lebanon), the Bioinformatics Organization (USA), and the International Association of Engineers (IAENG). Co-authored books on intelligent systems for breast cancer and rheumatoid arthritis diagnosis.
Selected peer-reviewed publications from the last 5 years
Journal of Imaging Informatics in Medicine
Journal of X-Ray Science and Technology, 33 (3), 608-620
Physiological Measurement, 45 (2), 02TR01
Applied Computational Intelligence and Soft Computing, 2017 (1), 3048181
Open-source AI solutions advancing healthcare and medical research
Knee Osteoarthritis (KOA) diagnosis using Wide ResNet-50-2 for KL severity grading. Achieved 72% accuracy with transfer learning on OAI dataset.
View ProjectMobile Image-to-Image Translation system based on CycleGAN for unpaired image-to-image translation with practical medical applications.
View ProjectGamma Ray Nuclides Identification Using Residual Learning for nuclear spectroscopy analysis with applications in medical physics.
View ProjectAI system to summarize and simplify medical reports generated by doctors or medical devices for better patient understanding using NLP.
View ProjectDeep Learning for Fashion Classification using the DeepFashion dataset with Category and Attributes Prediction — demonstrating transfer learning expertise.
View ProjectCervical Intraepithelial Neoplasia (CIN1-3) Disease Grading Using a Mixture of Experts Approach — published research with open-source implementation.
View ProjectI welcome collaborations with researchers, healthcare institutions, and companies looking to leverage cutting-edge AI in medicine. With extensive experience in developing AI solutions for healthcare, I can help transform your medical data into actionable insights.
Development and fine-tuning of Large Language Models for clinical documentation, diagnosis support, and medical Q&A systems.
Vision-Language Models for radiology report generation, medical image captioning, and multimodal diagnostic AI.
Deep learning solutions for X-ray, CT, MRI analysis including detection, segmentation, and classification tasks.
AI-powered systems for disease grading, risk prediction, and treatment recommendation based on patient data.
Analysis of ECG, EEG, and other physiological signals using machine learning for diagnosis and monitoring.
Expert guidance on AI/ML research methodology, dataset curation, model selection, and publication strategy.