Fahmi Khalifa received his BS and MS degrees in Electronics and Electrical Communication Engineering from Mansoura University, Egypt in 2003 and 2007, respectively. He received his PhD degree in 2014 in Electrical Engineering from the Electrical and Computer Engineering Department (ECE), University of Louisville (UofL), USA. Dr. Khalifa has more than 14 years of hands-on experience in the fields of Artificial Intelligence, image/signal processing, machine learning, biomedical data analysis, and computer-aided diagnosis. Dr. Khalifa has more than 150 peer-reviewed publications appearing in prestigious journals and top-ranked international conferences in addition to five US patents. Dr. Khalifa's honors and awards include Mansoura University scholarship for distinctive undergraduate students for four consecutive years, Theobald Scholarship Award in 2013 (ECE, UofL), the ECE Outstanding Student award for two times in 2012 and 2014 (ECE, UofL), the John M. Houchens award for the outstanding dissertation (UofL), the second-place Post-Doctoral Fellow award in 2014 Research! Louisville, UofL. He was the recipient of the PowerLIVE Award for Faculty commitment to students and their academic success at Morgan State University, 2023. 

Selected Publications:

• F. Khalifa, A. Shalaby, A. Soliman, S. Elaskary, A. Refaey, and M. Abdelazim, “Artificial intelligence-based computer-aided diagnosis applications for brain disorders from medical imaging data Volume II” Frontiers in Neuroscience: Brain Imaging, vol. 17: 1241926 (5 pp), 2023. DOI: https: // 10. 3389/ fnins. 2023. 1241926[
• O. Akinniyi, Md M. Rahman, H. S. Sandhu, A. El-Baz, and F. Khalifa, “Multi-stage classification of retinal OCT using multi-scale ensemble deep architecture” Bioengineering, vol. 10, no. 7 (16 pp.), 2023.
•  P. Sharma, A. Naglah, S. Aslan, F. Khalifa, A. El-Baz, S. Harkema, and J. D’Amico, “Preservation of functional descending input to paralyzed upper extremity muscles in motor complete cervical spinal cord injury,” Clinical Neurophysiology, vol. 150, no. 1, pp. 56–68, 2023
• I. Razzak, M. K. Khan, G. Xu, and F. Khalifa, “Guest Editorial Open and Interpretable AI in Computational Pathology,” IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 4, pp. 1657–1660, 2023.
• A. Shalaby, A. Soliman, S. Elaskary, A. Refaey, M. Abdelazim, and F. Khalifa, “Editorial: Artificial intelligence-based computer-aided diagnosis applications for brain disorders from medical imaging data,” Frontiers in Neuroscience: Brain Imaging, vol. 37: 998818 (4 pp), 2023.
• I. 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, 2023, DOI: https: // doi. org/ 10. 1109/ TCBB. 2022. 3219032
• M. Elgeneedy, F. Khalifa, H. E. Moustafa, H. Khater, and E. AbdElhalim, “An MRI- based deep learning approach for accurate detection of Alzheimer’s disease,” Alexandria Engineering Journal, vol. 63, no. 1, pp. 211–221, 2023.
• O. Akinniyi, I. Razzak, Md M. Rahman, H. S. Sandhu, A. El-Baz, and F. Khalifa, “Multi-classification of retinal diseases using a pyramidal ensemble deep framework,” In: Proceedings of IEEE International Conference on Image Processing (ICIP), Kuala Lampur, Malaysia, October 8–11, 2023.
• A. Aboudessouki, K. M. Ali, M. Elsharkawy, A. Alksas, A. Mahmoud, F. Khalifa, M. Ghazal, J. Yousaf, H. Abu Khalifeh, and A. El Baz, “Automated diagnosis of breast cancer using deep learning-based whole slide image analysis of molecular biomarkers,” In: Proceedings of IEEE International Conference on Image Processing (ICIP), Kuala Lampur, Malaysia, October 8–11, 2023.
• A. El-Baz, D. Gondim, A. Naglah, and F. Khalifa, “Systems and methods for digital transformation of medical images and fibrosis detection,” Patent Application # 17/845,880, December 2022.