Biomedical AI Research & Innovation Lab

AI/ML laboratory for multimodal biomedical and biological data analysis using state-of-the-art AI/ML tools supported by AIM-AHEAD Program for AI Readiness (PAIR) NIH Agreement # 1OT2OD032581

The AI/ML health equity research lab focus is on the intersection of biomedical informatics and imaging science through the application of state-of-the-art AI/ML. The overarching goal is to translate these methods into practice through applications to improve systems by mitigating bias and investigating explainable models in image classification, region-of-interest (ROI) detection (segmentation/object detection), automatic interpretation, concept generation, cross and multimodal retrievals of biomedical images, etc. Another aim of this proposal is to expose minorities and underrepresented groups including students in STEM environments and community partners to the AI/ML field to tackle and address disparities and minority health in behavioral health, cardiometabolic health, and cancer. With the strong partnership between engineering, computer science, and biology departments, this will build a community of multidisciplinary researchers in AI/ML focused on addressing minority health and health disparities. 

A collaborative cross-disciplinary team with complementary research expertise and access to excellent biomedical and biological research at Morgan State University (MSU). 

1- The research group has three accepted papers to present at the International Conference on Computing and Machine Intelligence, April 2025

2- The research team has two accepted manuscripts for the collaborative research project in Alzheimer’s Disease and Text-Guided Synthesis in Medical Multimedia Retrieval (March 2025).

3- The PI was a Keynote Speaker at

a. International Conference on Intelligent Systems, Blockchain, and Communication Technologies, Sharm El-Sheikh, Egypt, July 2024,

b. International Conference on Computing and Machine Intelligence (ICMI), Central Michigan University, April 2024

4- The PI has an Invited Speaker @National Symposium on Equitable AI, Pikesville, Maryland, USA, April 2024

The research focus is to develop appropriate AI/ML tools to provide enough abstract information by analyzing multimodal data to assess organ functionality, or detect abnormalities (e.g., cancers), or identify predictors for progression to advanced disease stages (e.g., retinal diseases). We build efficient hybrid learning and fusion methods for better diagnosis using explainable modules with capability to interpret decisions and  mimic physician’s perceptions. Our research spans various applications and domains (e.g., brain, breast cancer, prostate cancer). Recent publications

PowerLIVE Award, Morgan State University, December 2023

Dr. Khalifa was awarded the Faculty Award for commitment to students and their academic success https://www.msupowerlive.org/2023-awardees

Fahmi Khalifa is an assistance professor of Electrical and Computer Engineering (ECE), School of Engineering, MSU. Dr. Khalifa’s areas of research include AI in medicine, medical image analysis, image and signal processing, and machine learning. He has more than 16 years of hands-on experience in the fields of artificial intelligence, image/signal processing, machine learning, biomedical data analysis, and computer-aided diagnosis with more than 200 publications appearing in prestigious journals and top-rank international conferences in addition to and five US patents. 

Md Mahmoudur Rahman is a professor of computer science (CS), School of Computer, Mathematical & Natural Sciences, MSU His areas of research include AI in healthcare, computer vision, image processing, information retrieval, machine learning, and data mining. Dr. Rahman has demonstrated numerous ways to foster research involvement of underserved (both women and African American/Black) undergraduate and graduate students for several NSF and internally funded projects in the field of Medical Imaging Informatics with application of AI/ML.  

 

Timothy Meeker an Assistant Professor of Biology at MSU where he works on two NIH-funded programs at MSU: A Student-Centered, Entrepreneurship Development (ASCEND) and Research Centers for Minority Institutes (RCMI) and is leading a research project in the renewal of the RCMI cooperative agreement at MSU. His work investigates the neural mechanisms associated with racial disparities in pain sensitivity in healthy people and patients with neuropathic pain. 

 

• O. T. Adeniran, B. Ojeme, T. Ajibola, O. E. Peter,  A. O. Ajala, M. Rahman, and F. Khalifa, “Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer’s Disease Detection,” Algorithms, 2025 (Accepted)

• A. Saleh, A. H. Rabie, S. E. ElSayyad, A. Takieldeen, and F. Khalifa, “An optimized ensemble grey wolf-based pipeline for monkeypox diagnosis,” Scientific Reports, vol. 15:3819 (23 pp.), 2025. 

• O. O. E. Peter, O.T. Adeniran, A. M.G. John-Otumu, Fahmi Khalifa, and Md Mahmudur Rahman, “Text-Guided Synthesis in Medical Multimedia Retrieval: A Framework for Enhanced Colonoscopy Image Classification and Segmentation,” Algorithms, 2025 (Accepted)

• Abdelhaiem, A. Alkasas, H. Balaha, M. Badawy, N. Alghamdi, M. Ghazal, S. Contractor, E. van Bogaert, D. Gondim, S. Silva, F. Khalifa, and A El-Baz, “Incorporating imaging, clinical, pathology, and demographic markers for hormonal therapy prediction in prostate cancer,” IEEE Access, vol. 11, pp. 195960–195973, 2024

• Abdelhaiem, M. A. Badawy, M. Abou El-Ghar, M. Ghazal, S. Contractor, E. van Bogaert, D. Gondim, S. Silva, F. Khalifa, and A El-Baz, “Multi-branch CNNFormer: a novel framework for predicting prostate cancer response to hormonal therapy,” BioMedical Engineering OnLine, vol. 24, no. 131, pp. 1-15, 2024.

• Elnakib, F. Khalifa, A. Soliman, A. Shalaby, and M. Elhosseini, “Editorial: Emerging Artificial Intelligence Technologies for Neurological and Neuropsychiatric Research,” Frontiers in Neuroscience: Brain Imaging, vol. 18: 1518442 (3 pp), 2024.

• Razzak, S. Naz, H. Alinejad-Rokny, T. N. Nguyen, and F. Khalifa, “A cascaded mutliresolution ensemble deep learning framework for large scale Alzheimer’s disease detection using brain MRIs,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 21, no. 4, pp. 573–581, 2024.

• Aina, O. Akinnyi, Md. M. Rahman, V. Odero-Marah, and F. Khalifa, “A hybrid learning architecture for mental disorder detection using emotion recognition,” IEEE Access, vol. 12, pp. 91410–91425, 2024.

• Dixon, O. Akinniyi, A. Abdelhamid, G. A. Saleh, Md. M. Rahman, and F. Khalifa, “A hybrid learning-architecture for improved brain tumor recognition,” Algorithms, vol. 17, no. 16(221)–17 pp., 2024.