ADVANCEMENTS IN MACHINE LEARNING FOR EARLY DETECTION OF NEUROLOGICAL DISORDERS: FOCUS ON MRI-BASED DIAGNOSTICS

Abstract

Globally, the most frequent reasons for disability are brain diseases such multiple sclerosis, Brain Tumor, and Alzheimer's disease. In an effort to reduce the progression of the disease and improve patient outcomes, early identification is essential. Magnetic resonance imaging (MRI) which is a non-invasive imaging method provides important information on anatomical and functional alterations in the brain, making it a potentially useful tool for early diagnosis. The execution of machine learning (ML) algorithms to MRI data for the early identification of neurological illnesses is examined in this thesis. The results show that CNN-based models perform better than other strategies in detecting early illness signs, yielding notable gains in accuracy when compared to conventional diagnostic techniques. The thesis also discusses problems including class imbalance, data heterogeneity, and the interpretability of machine learning models, offering solutions. This study demonstrates the promise for automated, scalable, and precise diagnostic tools to support physicians in the early identification and intervention of neurological illnesses by fusing machine learning with MRI data. Expanding datasets, integrating multimodal data, and enhancing model generalizability to actual clinical settings will be the main goals of future research.