تسجيل الدخول / التسجيل

صلاح محمد عواد

مدرس دكتور

تكنولوجيا المعلومات والاتصالات

المعهدالتقني / السماوة

السيرة الذاتية

Dr. Salah Alheejawi is a Lecturer Assistant at Al-Furat Al-Awsat University, Samawah Institute, Iraq. He previously served as a Postdoctoral Fellow at Northeastern University, USA, where he contributed to advanced research in artificial intelligence and biomedical imaging. He earned his Ph.D. from the University of Alberta, Canada, and holds an M.Tech. in Communication and Information Systems from Aligarh Muslim University, India. His research interests include machine learning, medical image analysis, pattern recognition, and computer vision.

الاهتمامات البحثية

  • My research revolves around the intersection of artificial intelligence, deep learning, and medical image analysis, with a particular focus on digital pathology and AI-driven diagnostics. Over the years, I have worked extensively on developing machine learning models to automate and enhance biomedical imaging interpretation, cancer detection, and abnormality classification, with the ultimate goal of improving healthcare outcomes and advancing computational medicine. During my PhD at the University of Alberta, I explored medical image processing, computer vision, and natural language processing (NLP), applying AI-driven methodologies to various biomedical challenges. My work has contributed to pioneering advancements in histopathological image segmentation, tumor grading, and predictive modeling. I have leveraged deep learning architectures, such as CNNs, U-Net, and GANs, to refine feature extraction, enhance classification accuracy, and optimize computational frameworks.

Scientific Research

  • Alheejawi, S., Xu, H., Berendt, R., Jha, N., & Mandal, M. (2018). Novel lymph node segmentation and proliferation index measurement for skin melanoma biopsy images. Computerized Medical Imaging and Graphics, 73, 19–29.
  • Alheejawi, S., Xu, H., Berendt, R., Jha, N., & Mandal, M. (2018). Novel lymph node segmentation and proliferation index measurement for skin melanoma biopsy images. Computerized Medical Imaging and Graphics, 73, 19–29.
  • Alheejawi, S., Berendt, R., Jha, N., & Mandal, M. (2021). Detection of metastatic melanoma in (H&E)-stained images using deep learning techniques. Tissue and Cell, 73, 101659
  • Alheejawi, S., Berendt, R., Jha, N., Maiti, S. P., & Mandal, M. (2021). Automated Proliferation Index Calculation for Skin Melanoma Biopsy Images Using Machine Learning. Computerized Medical Imaging and Graphics, 89, 101893.
  • Mandeel, T. H., Alheejawi, S., & Naji, S. (2021). Pneumonia binary classification using multi-scale feature classification network on chest X-ray images. IAES International Journal of Artificial Intelligence, 11(4), 1469.
  • Alheejawi, S., Berendt, R., Jha, N., & Mandal, M. (2020). Melanoma Cell Detection in Lymph Nodes Histopathological Images Using Deep Learning. Signal & Image Processing: An International Journal (SIPIJ), 11(4).
  • Jain, M. N., Al-Heejawi, S. M. A., Azzi, J. R., & Amal, S. (2025). Digital Pathology and Ensemble Deep Learning for Kidney Cancer Diagnosis: Dartmouth Kidney Cancer Histology Dataset. Applied Biosciences, 4(1), 8
  • Albadri, R. F., Awad, S. M., Hameed, A. S., et al. (2024). A Diabetes Prediction Model Using Hybrid Machine Learning Algorithm. Mathematical Modelling of Engineering Problems, 11, 2119– 2126.
  • Mudavadkar, S. G. R., Deng, M., & Al-Heejawi, S. M. A. (2024). Gastric Cancer Detection with Ensemble Learning on Digital Pathology. Diagnostics, 4
  • Kondejkar, T., Al-Heejawi, S. M. A., Breggia, A., et al. (2024). Multi-Scale Digital Pathology Patch-Level Prostate Cancer Grading Using Deep Learning. Bioengineering, 11(6), 624
  • Balasubramanian, A. A., Al-Heejawi, S. M. A., Singh, A., et al. (2024). Ensemble Deep Learning- Based Image Classification for Breast Cancer Diagnosis. Cancers, 16(12), 2222.

    لايوجد تدوينات