Mrs. Azizeh Akbari | fMRI Images | Best Researcher Award
Research Assistant at University of Coimbra, Portugal
Azizeh Akbari is a biomedical engineering researcher specializing in neuroimaging and artificial intelligence applications in medical diagnostics. With a robust background in neuroscience and deep learning, she has contributed to pioneering research in Alzheimer’s disease diagnosis using fMRI and structural imaging. Azizeh combines her engineering foundation with clinical insight to tackle complex healthcare challenges, advancing early detection systems through cutting-edge AI-driven models. Her work has earned recognition in international research communities, including high-impact journal publications and performance accolades in global AI competitions.
Profile
Education
Azizeh pursued her academic journey in biomedical engineering, beginning with a Bachelor’s degree from Ragheb Isfahani University, Iran, where she built a solid foundation in medical technologies. She continued with a Master’s degree at Hakim Sabzevari University, achieving a perfect GPA of 4.0 and earning the top academic rank among her peers. Her thesis focused on the classification of Alzheimer’s disease stages using fMRI images through transfer learning and VGG-16 deep networks, under the supervision of Dr. Javad Haddadnia. In 2025, she was awarded a research fellowship in neuroscience at the University of Coimbra, Portugal, furthering her expertise in brain imaging and cognitive health diagnostics.
Experience
Azizeh has engaged in a range of academic and professional experiences that showcase her interdisciplinary skills. She is currently involved in fMRI image analysis for Alzheimer’s disease diagnosis, applying deep learning techniques to neurological data. From 2020 to 2022, she worked with Tecvico Company on machine learning projects for cancer prognosis, including TNM staging and survival prediction in head and neck cancers. Her responsibilities spanned algorithm development, radiomics feature extraction, and result validation. Additionally, she has interned at Askaria Hospital and Tajhiz Teb Paya Company, gaining hands-on experience with medical devices and wound therapy systems, and has served as an educator at an NGO, sharing her knowledge with future innovators.
Research Interest
Azizeh’s primary research interests lie at the intersection of medical imaging, neuroscience, and artificial intelligence. Her work emphasizes using PET, MRI, CT, and particularly fMRI to understand brain function and structure. She is especially passionate about applying deep learning and machine learning algorithms to detect, classify, and monitor neurological disorders, with a focus on Alzheimer’s disease. Her broader interests include radiomics, survival prediction, and medical image fusion techniques, aiming to create predictive models that improve clinical decision-making and patient outcomes.
Award
Azizeh has been recognized for her academic and professional excellence through several awards. She ranked first in GPA among graduate students in her department at Hakim Sabzevari University, reflecting her dedication and consistent performance. Additionally, she gained international recognition by placing fourth in the HECKTOR Challenge 2021 (Task 2) alongside her Tecvico company team. This competition focused on using AI and radiomics in cancer prediction, and her team’s solution was noted for its robustness and accuracy in TNM staging and survival modeling.
Publication
Azizeh Akbari has contributed to several impactful publications in the field of neuroimaging and machine learning:
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Nasrolahzadeh, M., Akbari, A. (2025). Selection, Visualization, and Explanation of Deep Features from resting-state fMRI for Alzheimer’s Disease Diagnosis. Psychiatry Research: Neuroimaging. [Cited by 4 articles]
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Akbari, A., Nasrolahzadeh, M., Haddadnia, J. (2024). Early Diagnosis of Alzheimer’s Disease from Structural fMRI images based on Deep Learning Networks and Transfer Learning Approach. Journal of Multimedia Tools and Applications (Submitted). [Cited by 2 articles]
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Salmanpour, M.R., Hosseinzadeh, M., Akbari, A., et al. (2022). Prediction of TNM Stage in Head and Neck Cancer Using Hybrid Machine Learning Systems and Radiomics Features. SPIE Medical Imaging. [Cited by 9 articles]
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Salmanpour, M.R., Hosseinzadeh, M., Modiri, E., Akbari, A., et al. (2022). Advanced Survival Prediction in Head and Neck Cancer Using Hybrid Machine Learning Systems and Radiomics Features. SPIE Biomedical Applications. [Cited by 11 articles]
These works highlight her cross-domain capability in applying AI to both neurological and oncological medical imaging problems.
Conclusion
Azizeh Akbari stands out as a driven and visionary researcher at the forefront of biomedical engineering and neuroscience. Her academic achievements, technical expertise, and collaborative research contributions reflect a strong commitment to innovation in healthcare technologies. Through her current fellowship in neuroscience and past machine learning projects, she continues to explore transformative solutions for disease diagnosis and prognosis. Her passion for merging deep learning with neuroimaging defines her future trajectory as a scholar dedicated to enhancing patient care through intelligent diagnostics.