Comprehensive Summary
This study by Abbasabadi et al. investigates how transfer learning (TL) approaches can enhance Alzheimer’s disease (AD) classification techniques by using resting-state functional magnetic resonance imaging (rs-fMRI). Data from the Alzheimer’s Disease Neuroimaging Initiative were used, including 97 participants split between AD patients and normal controls. The three pre-trained deep learning models including VGG19, ResNet50, and AlexNet were then used by the investigators after extensive preprocessing of the obtained imaging data. The results indicated that AlexNet had the highest overall classification accuracy (98.71%) among the network methods, compared to other networks; which showed this to be the case with ResNet50 (98.20%) followed by VGG19 (96.91%). Other performance parameters, such as precision, recall, and F1-score validate AlexNet’s superior classification performance. However, while AlexNet provided the highest accuracy, ResNet50 generated more interpretable Grad-CAM attention maps, pinpointing brain regions associated with AD. This combination of high accuracy and interpretability demonstrates the potential of TL models in advancing neuroimaging analysis.
Outcomes and Implications
The ability to accurately and efficiently differentiate between AD patients and healthy individuals is critical given the disease’s high prevalence and the difficulty of early diagnosis. Present diagnostic techniques that heavily depend on manual image analysis can be laborious, inconsistent, and error-prone. By employing pre-trained deep learning models, this work opens a new path towards improving the accuracy and automatization of diagnostic tools to aid clinicians in early and consistent diagnosis of AD. The results indicate that one day, AI-based rs-fMRI analysis may facilitate clinical judgments, minimize diagnostic variance, and provide quicker access to timely treatment. Additional validation and incorporation of these methods into the daily workflow may add to current diagnostic techniques as evidence of efficacy and ultimately benefit at risk persons or persons living with Alzheimer's disease.