العربية 
Sana’a – In a distinguished academic achievement that bridges technology and medicine, female students from the Software Engineering program at the College of Computing and Information Technology at the University of Science and Technology (UST) in Sana’a presented an outstanding graduation project titled:
“Intelligent System for Diagnosing Multiple Sclerosis Using Brain MRI”
The project aims to design and develop an intelligent system that supports the diagnosis of Multiple Sclerosis (MS) by analyzing Magnetic Resonance Imaging (MRI) scans using artificial intelligence and deep learning techniques.
Multiple Sclerosis is a chronic neurological disorder that affects the central nervous system due to the immune system attacking the myelin sheath surrounding nerve fibers, resulting in brain lesions that impact movement, sensation, vision, and cognitive functions.
The diagnostic challenge lies in the absence of a definitive medical test, as diagnosis typically depends on clinical examination and MRI analysis, in addition to internationally recognized diagnostic criteria such as the McDonald and Barkhof/Tintoré criteria.
In response to these challenges, the students sought to develop an intelligent system capable of:
- Automatically detecting MS lesions in MRI scans
- Analyzing lesion characteristics including count, size, shape, and orientation
- Classifying cases into three categories: definite, probable, or possible diagnosis
- Generating a comprehensive medical report automatically and integrating the results with the Electronic Health Record (EHR)
- Comparing current scans with previous ones to monitor disease progression
The project was implemented according to the Waterfall methodology, beginning with requirements gathering and analysis, followed by system and database design, and concluding with implementation, testing, and deployment.
The students used Python to develop the AI model leveraging deep learning algorithms—specifically Convolutional Neural Networks (CNNs)—as well as React for the user interface, Flask for system integration through REST APIs, and MySQL for storing patient data and medical reports.
The significance of this project lies in its support for early and accurate diagnosis, reducing the burden on neurologists, particularly in regions facing shortages of specialized medical staff. It also represents an important step toward integrating artificial intelligence into the healthcare sector locally, with potential for future expansion to medical centers within Yemen and beyond. The project team worked under the supervision of Prof. Sadeq Al-Tawil.
This project stands as a commendable example of the accomplishments of students at the College of Computing and Information Technology at the WWW, reaffirming the capability of young talent to develop innovative technological solutions that serve society and advance the healthcare sector.
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