Published on by Oncology

Assist. Prof Dr. Dina M. Ibrahim | Engineering | Editorial Board Member 

Assistant Professor | Tanta University | Egypt

She is a highly cited interdisciplinary researcher whose work bridges oncology-focused medical imaging with advanced machine learning, deep learning, wireless networks, and intelligent systems, demonstrating strong impact across healthcare, engineering, and applied artificial intelligence. With 1,848 citations, an h-index of 18, and an i10-index of 27, her research portfolio reflects sustained scholarly influence since 2020 and strong international collaboration. Her most influential contributions are in AI-driven disease diagnosis, particularly deep learning models for chest X-ray and MRI analysis, where her work on multi-class classification of COVID-19, pneumonia, and lung cancer has gained wide recognition and adoption in clinical research contexts. She has also made notable advances in diabetic eye disease detection, brain MRI synthesis using GANs, and hybrid deep learning architectures for biomedical signal and image analysis, supporting early diagnosis and decision-making in oncology and related medical fields. Beyond healthcare, her research extends to TinyML and edge AI for ultra-low-power devices, IoT systems, cybersecurity, wireless sensor networks, and energy consumption prediction, highlighting her ability to translate core AI techniques across domains. She has collaborated with leading academics from universities in Saudi Arabia, Egypt, the UK, and Europe, contributing to high-impact journals such as Computers in Biology and Medicine, Diagnostics, Sensors, IEEE Access, Micromachines, Sustainability, and PeerJ Computer Science. Her work is characterized by methodological rigor, practical relevance, and a strong focus on real-world deployment, particularly in resource-constrained and data-driven environments. Through sustained publication, mentoring, and cross-disciplinary collaboration, she continues to advance intelligent healthcare systems, strengthen AI applications in oncology-related diagnostics, and contribute to global innovation at the intersection of computer science, engineering, and medical research.

Profile: Google Scholar

Featured Publications

  1. Ibrahim, D. M., Elshennawy, N. M., & Sarhan, A. M. (2021). Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases. Computers in Biology and Medicine, 132, 104348.

  2. Elshennawy, N. M., & Ibrahim, D. M. (2020). Deep-pneumonia framework using deep learning models based on chest X-ray images. Diagnostics, 10(9), 649.

  3. Alajlan, N. N., & Ibrahim, D. M. (2022). TinyML: Enabling inference deep learning models on ultra-low-power IoT edge devices for AI applications. Micromachines, 13(6), 851.

  4. Al-Shargabi, A. A., Almhafdy, A., Ibrahim, D. M., Alghieth, M., & Chiclana, F. (2022). Buildings’ energy consumption prediction models based on buildings’ characteristics: Research trends, taxonomy, and performance measures. Journal of Building Engineering, 54, 104577.

  5. Alrashedy, H. H. N., Almansour, A. F., Ibrahim, D. M., & Hammoudeh, M. A. A. (2022). BrainGAN: Brain MRI image generation and classification framework using GAN architectures and CNN models. Sensors, 22(11), 4297.

Her work advances global innovation by integrating deep learning and intelligent systems into medical imaging, enabling earlier, more accurate diagnosis of cancer and other critical diseases. By translating AI research into practical healthcare, IoT, and edge-computing solutions, she strengthens data-driven decision-making, supports resource-efficient technologies, and bridges the gap between scientific discovery, clinical practice, and industry needs worldwide.

 

Dina M. Ibrahim | Engineering | Editorial Board Member

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