Redha Ali
Redha Ali
Research Assistant
Redha Ali received his B.S. in Computer Science and Information Technology from the College of Electronic Technology, Bani Walid, Libya, in 2012. He completed his M.S. in Electrical and Computer Engineering from the University of Dayton in 2016. His Master's thesis work and publication are in the field of image and video denoising. He is currently pursuing his Ph. D. research in medical image classification at the University of Dayton. His applied research interests include medical image processing, deep learning, machine learning, computer vision, video restoration, and enhancement.
Research Interests
Medical Image Classification, Medical Image Segmentation, Image and Video Restoration, Image and Video Enhancement, Object Detection
Academic Affiliation
Electrical and Computer Engineering Department, University of Dayton
Master of Science (M.S.)
Electrical and Computer Engineering, University of Dayton
Bachelor of Science (B.S)
Computer Science and Information Technology, College of Electronics Technology
- Redha Ali, Hailong Li, Jonathan R. Dillman, Mekibib Altaye, Hui Wang, Nehal A. Parikh, and Lili He. A self-training deep neural network for early prediction of cognitive deficits in very preterm infants using brain functional connectome data. Pediatric Radiology. Pubmed Journal
- Ali, R., Hardie, R. Recursive non-local means filter for video denoising. J Image Video Proc. 2017, 29 (2017). https://doi.org/10.1186/s13640-017-0177-2 PMC Journal
- Ali, R., Hardie, R. C., Narayanan, B. N., & De Silva, S. (2019, July). Deep learning ensemble methods for skin lesion analysis towards melanoma detection. In 2019 IEEE National Aerospace and Electronics Conference (NAECON) (pp. 311-316). IEEE.. Journal
- Ali, R., Hardie, R. C., De Silva, M. S., & Kebede, T. M. (2019). Skin lesion segmentation and classification for ISIC 2018 by combining deep CNN and handcrafted features. arXiv preprint arXiv:1908.05730. Journal