Biomed Phys Eng Express. 2025 Nov 24. doi: 10.1088/2057-1976/ae2336. Online ahead of print.
ABSTRACT
Leukodystrophies are a group of inherited disorders that predominantly and selectively affect the white matter of the central nervous system. Their overlapping clinical and imaging manifestations make a timely and accurate diagnosis challenging. In this study, brain MRI images from 115 patients with confirmed Leukodystrophy representing five major subtypes were analyzed. The imaging pipeline began with comprehensive pre-processing, which included tilt correction, noise reduction, skull stripping, brain segmentation, intensity normalization, and registration. This process ensured consistency throughout the dataset.
 

Subsequently, two main classification strategies were investigated: (1) five traditional machine learning algorithms trained on four sets of handcrafted features extracted from the white matter and whole-brain regions, and (2) deep learning models using pre-trained convolutional neural networks fine-tuned on 3D MRI volumes. The CNN-based methods consistently outperformed traditional approaches, demonstrating a greater ability to learn complex hierarchical and spatial patterns. The InceptionV3 architecture achieved the highest performance on whole-brain images, with an accuracy of 93.41%, precision of 85.49%, recall of 83.95%, specificity of 95.77%, F1-score of 84.48%, and AUC of 89.86%. These findings indicate that machine learning-based approaches provide a reliable automated tool that can support neurologists in the differential diagnosis of Leukodystrophies, facilitating targeted confirmatory genetic testing and guiding patient management strategies.
PMID:41285048 | DOI:10.1088/2057-1976/ae2336
