عضو هيئة تدريسية -مسؤول شعبة ضمان الجودة أ.م.د. علي مجيد حسن

Ali M. Hasan born in 1980. He received his B.Sc and M.Sc degree in Control and System Engineering from University of Technology, Iraq in 2002 and 2004 respectively. He received his Ph.D degree in 2017 from University of Salford, United Kingdom in the field of medical image processing. His research interests include image processing and segmentation. Present, he is working as an assistant professor in Al-Nahrain University, Iraq.

1. Doctoral Degree (PhD) in Computer Engineering (2017) from University of Salford, Manchester, United Kingdom. 2. Master Degree (MSc) in Computer Engineering (2004) from University of Technology, Baghdad, IRAQ. 3. Bachelor Degree (BSc) in Control and Systems Engineering (2002) from University of Technology, Baghdad, IRAQ.

Statistical Analysis (SPSS) and Mathematics / PhD students

Computer Vision and Image Processing

1. Hamid Jalab and Ali Hasan (2012), Image retrieval system based on wavelet network. International Conference on Computer, Information and Telecommunication Systems (CITS), 2012 (pp. 1-4). IEEE. 2. Ali Hasan, Farid Meziane and Mohammad Abd Kadhim (2016), Automated Segmentation of Tumours in MRI Brain Scans. In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016)- Volume 2: BIOIMAGING, Italy. pages 55-62 ISBN: 978-989-758-170-0. 3. Ali Hasan, Farid Meziane and Hamid Jalab (2016), Performance of Grey Level Statistic Features versus Gabor wavelet for Screening MRI Brain Tumors: A Comparative Study, 6th International Conference on Information Communication and Management, University of Hertfordshire, UK, October 29-31, 2016. 4. Hamid Jalab, Zahra Moghaddasi, Ali Hasan and Zouhir Wakaf (2016), Image Splicing Detection using Electromagnetism like Based Descriptor. In Intelligent Systems Conference (SAI), UK, September, 2016. 5. Ali Hasan, Farid Meziane, Rob Aspin and Hamid A. Jalab (2017), MRI Brain Scan Classification Using Novel 3-D Statistical Features, 2nd International Conference on Internet of Things, Data and Cloud Computing, University of Cambridge, UK, March 22-23, 2017. 6. Hamid Jalab, Rabha Ibrahim, Abdullah Jalab, Dania Jalab and Ali Hasan (2017), Medical Image Enhancement Based on Statistical Distributions in Fractional Calculus, In Computing Conference, UK, July, 2017. 7. Alaa Ahmed Abbas Al-abayechi, Hamid A. Jalab, Rabha W. Ibrahim, Ali M. Hasan, Image enhancement based on fractional Poisson for segmentation of skin lesions using the watershed transform, In the 5th International Visual Informatics Conference 2017, Bangi, Salangor, Malaysia. LNCS Volume 10645 of the lecture notes in computer science series. Springer. 8. Ali Hasan and Farid Meziane (2016), Automated screening of MRI brain scanning using grey level statistics. Computers & Electrical Engineering, Volume 53, July 2016, Pages 276–291. 9. Ali Hasan, Farid Meziane, Rob Aspin and Hamid A. Jalab (2016), Segmentation of Brain Tumors in MRI Images Using Three-Dimensional Active Contour without Edge, Symmetry 2016, 8(11), 132; doi:10.3390/sym8110132. 10. Ali Hasan, Hamid Jalab (2018), A new deformable model based on fractional Wright energy function for tumor segmentation of volumetric brain MRI scans, Computer Methods and Programs in Biomedicine 2018, 163(2018), Pages 21-28. 11. Ali Hasan (2018), A Hybrid Approach of Using Particle Swarm Optimization and Volumetric Active Contour without Edge for Segmenting Brain Tumors in MRI Scan, Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 2018, 6(3). 12. H. A. Jalab, and A. M. Hasan, “Magnetic Resonance Imaging Segmentation Techniques of Brain Tumors: A Review,” Arch Neurosci, vol. special issue, no. 6, pp. e84920, 2019. 13. Hasan, A.M., Jalab, H.A., Meziane, F., Kahtan, H. and Al-Ahmad, A.S., 2019. Combining deep and handcrafted image features for MRI brain scan classification. IEEE Access, 7, pp.79959-79967. 14. Hasan, A.M., AL-Jawad, M.M., Jalab, H.A., Shaiba, H., Ibrahim, R.W. and AL-Shamasneh, A.A.R., 2020. Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features. Entropy, 22(5), p.517. 15. Hasan, A.M.; Jalab, H.A.; Ibrahim, R.W.; Meziane, F.; AL-Shamasneh, A.R.; Obaiys, S.J. MRI Brain Classification Using the Quantum Entropy LBP and Deep-Learning-Based Features. Entropy 2020, 22, 1033. 16. Omer, H.K., Jalab, H.A., Hasan, A.M. and Tawfiq, N.E., 2019. Combination of Local Binary Pattern and Face Geometric Features for Gender Classification from Face Images. In 2019 9th IEEE International Conference on Control System, Computing and Engineering (ICCSCE) (pp. 158-161). IEEE.