MRI-Based Multi-Class Classification of Rare Brain Disorders Using Deep Learning

Abstract

Rare brain disorder poses a big problem to diagnostics as it is not prevalent in the population, has confusing symptoms, and is not well-known in clinical practice. The classical forms of diagnostics do not support timely and accurate detection, postponing therapy and deteriorating clinical outcomes. As a solution to this problem, this paper builds a deep learning model to classify rare brain disorders in multiple classes by using MRI scans. The dataset consisted of 3,759 images of four disorders, namely Fukuyama Muscular Dystrophy (Fukuyama), Moyamoya Disease with Intraventricular Hemorrhage (Moyamoya), Pantothenate Kinase-Associated Neurodegeneration (PKAN), and Pachygyria with Cerebellar Hypoplasia (Pachygyria). The images were first preprocessed and resized and then divided into training, validation and testing cases, and tested in eight deep learning architectures, including Vision Transformer (ViT), DenseNet201, VGG19, MobileNetV2, Xception, ResNet152, InceptionV3, and EfficientNetB7. ViT was the most accurate, composing 99.47%, closely followed by VGG19 (98.40%) and MobileNetV2(98.01%). These findings support the idea that transformer-based and densely connected models are especially useful in modeling the complex spatial patterns of rare brain disorders. Altogether, the research shows that AI-based systems have the potential to assist clinicians in diagnosing rare neurological conditions more efficiently and accurately and ultimately contribute to better healthcare outcomes among underserved groups of patients.