Redha Ali

Bio

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. and Ph.D. in Electrical and Computer Engineering from the University of Dayton in 2016 and 2021, respectively. 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

• Object Detection

• Human Recognition

• Image and Video Restoration

• Image and Video Enhancement

Publications:

Journals

1. Ali, R., Li, H., Dillman, J.R. et al. (2022, Sep). A self-training deep neural network for early prediction of cognitive deficits in very preterm infants using brain functional connectome data. Pediatric Radiology (2022). https://doi.org/10.1007/s00247-022-05510-8.

2. Ali, R., Hardie, R. C., Narayanan, B. N., & Kebede, T. M. (2022, May). IMNets: Deep Learning Using an Incremental Modular Network Synthesis Approach for Medical Imaging Applications. Applied Sciences. 2022; 12(11):5500. https://doi.org/10.3390/app12115500.

3. Ali, R. A., & Hardie, R. C. (2017). Recursive non-local means filter for video denoising. EURASIP Journal on Image and Video Processing, 2017(1), 29. https://doi.org/10.1186/s13640-017-0177-2.

Conferences

1. Ali, R., & Ragb, H. K. (2021). Skin lesion segmentation and classification using deep learning and handcrafted features. arXiv preprint arXiv:2112.10307.

2. Ragb, H., Ali, R., Jera, E., & Buaossa, N. (2021). Convolutional neural network based on transfer learning for breast cancer screening. arXiv preprint arXiv:2112.11629.

3. Narayanan, B. N., De Silva, M. S., Hardie, R. C., & Ali, R. (2021, August). Ensemble Method of Lung Segmentation in Chest Radiographs. In NAECON 2021-IEEE National Aerospace and Electronics Conference (pp. 382-385). IEEE.

4. Ragb, H. K., Dover, I. T., & Ali, R. (2020, October). Deep Convolutional Neural Network Ensemble for Improved Malaria Parasite Detection. In 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) (pp. 1-10). IEEE.

5. Ali, R., Hardie, R. C., & Ragb, H. K. (2020, October). Ensemble lung segmentation system using deep neural networks. In 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) (pp. 1-5). IEEE.

6. Ragb, H. K., Dover, I. T., & Ali, R. (2020). Fused deep convolutional neural network for precision diagnosis of covid-19 using chest x-ray images. arXiv preprint arXiv:2009.08831.

7. 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.

8. Narayanan, B. N., Ali, R., & Hardie, R. C. (2019, September). Performance analysis of machine learning and deep learning architectures for malaria detection on cell images. In Applications of Machine Learning (Vol. 11139, p. 111390W). International Society for Optics and Photonics.

9. Narayanan, B. N., De Silva, M. S., Hardie, R. C., Kueterman, N. K., & Ali, R. (2019). Understanding Deep Neural Network Predictions for Medical Imaging Applications. arXiv preprint arXiv:1912.09621.

10. 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.

11. Narayanan, B. N., Krishnaraja, V., & Ali, R. (2019, July). Convolutional Neural Network for Classification of Histopathology Images for Breast Cancer Detection. In 2019 IEEE National Aerospace and Electronics Conference (NAECON) (pp. 291-295). IEEE.

12. Ali, R., & Ragb, H. K. (2019, July). Fused Deep Convolutional Neural Networks Based on Voting Approach for Efficient Object Classification. In 2019 IEEE National Aerospace and Electronics Conference (NAECON) (pp. 335-339). IEEE.

13. Ragb, H. K., Ali, R., & Asari, V. (2019, July). Aggregate Channel Features Based on Local Phase, Color, Texture, and Gradient Features for People Localization. In 2019 IEEE National Aerospace and Electronics Conference (NAECON) (pp. 351-355). IEEE.

14. Ali, R., Hardie, R., & Essa, A. (2018, July). A leaf recognition approach to plant classification using machine learning. In NAECON 2018-IEEE National Aerospace and Electronics Conference (pp. 431-434). IEEE.

15. Hardie, R. C., Ali, R., De Silva, M. S., & Kebede, T. M. (2018). Skin lesion segmentation and classification for ISIC 2018 using traditional classifiers with hand-crafted features. arXiv preprint arXiv:1807.07001.

16. Almahdi, R., & Hardie, R. C. (2016, July). Recursive non-local means filter for video denoising with Poisson-Gaussian noise. In 2016 IEEE National Aerospace and Electronics Conference (NAECON) and Ohio Innovation Summit (OIS) (pp. 318-322). IEEE.

Courses Taught:

• ECE 301 Introduction to MATLAB Programming

• MEE 214 Programming for Mechanical Engineers

• MTH 301 Matrix Theory and Application

• ECE 303L Signal and Systems Lab 

Licenses & Certifications 

• MATLAB Onramp

• Machine Learning Onramp

Awards:

• 5350$ Graduate Student Summer Fellowship 2018 - Scholarly excellence of research proposal

• Libyan Government Scholarship for Academic Excellency: fully paid Ph.D. degree 2017

• Libyan Government Scholarship for Academic Excellency: fully paid M.S. degree 2014

• The Intensive English Program Scholarship 2013 - Academic excellence  

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