الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
The thesis explores the use of computer intelligence in medicine, specifically in diagnosing multiple sclerosis using machine learning and deep learning techniques. Multiple sclerosis is a progressive autoimmune disease that targets the brain and spinal cord, causing inflammation, demyelination, and axonal loss. Diagnosis is challenging due to the risk of false diagnoses causing rapid progression of the disease. Machine learning and deep learning techniques, such as convolutional neural networks (CNNs), support vector machines (SVMs), and random forests (RFs), are employed to improve the accuracy and efficiency of MS diagnosis, ultimately improving patient outcomes and disease control. The study aims to influence machine learning and deep learning techniques to enhance early and precise diagnosis of MS.
Chapter 1:
It encompasses an introductory section and outlines the structure of the thesis.
Chapter 2:
The background chapter explores multiple sclerosis (MS), its origins, causative factors, symptoms, diagnostic procedures, and impact on individuals. It also discusses the role of MRI in MS detection and characterization, and the use of machine learning and deep learning techniques.
Chapter 3:
The literature review examines various papers on the subject, establishing a benchmark for comparison.
Chapter 4:
The methodology chapter outlines the proposed model’s workflow, including data acquisition, algorithms, and limitations.
Chapter 5:
The results chapter presents the results from the models and replicated algorithms from other literature review papers.
Chapter 6:
The Conclusion and Future Work chapter summarizes the research efforts and findings, outlining future actions to improve outcomes.