Multimodal classification of Alzheimer's disease and mild cognitive impairment using custom MKSCDDL kernel over CNN with transparent decision-making for explainable diagnosis

• Published in: Scientific reports 2024-01, Vol.14 (1), p.1774-1774, Article 1774
• Main Authors: Adarsh, V., Gangadharan, G. R., Fiore, Ugo, Zanetti, Paolo
• Format: Article
• Language: English
• Subjects: 692/700/1421/1628; 692/700/1421/65; Alzheimer Disease - diagnosis; Alzheimer's disease; Anatomy; Brain architecture; Classification; Cognitive ability; Cognitive Dysfunction - diagnosis; Decision making; Diagnosis; Humanities and Social Sciences; Humans; Magnetic Resonance Imaging - methods; multidisciplinary; Neural networks; Neural Networks, Computer; Neurodegenerative diseases; Neuroimaging; Neuroimaging - methods; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-52185-2

The study presents an innovative diagnostic framework that synergises Convolutional Neural Networks (CNNs) with a Multi-feature Kernel Supervised within-class-similar Discriminative Dictionary Learning (MKSCDDL). This integrative methodology is designed to facilitate the precise classification of individuals into categories of Alzheimer's Disease, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) statuses while also discerning the nuanced phases within the MCI spectrum. Our approach is distinguished by its robustness and interpretability, offering clinicians an exceptionally transparent tool for diagnosis and therapeutic strategy formulation. We use scandent decision trees to deal with the unpredictability and complexity of neuroimaging data. Considering that different people's brain scans are different, this enables the model to make more detailed individualised assessments and explains how the algorithm illuminates the specific neuroanatomical regions that are indicative of cognitive impairment. This explanation is beneficial for clinicians because it gives them concrete ideas for early intervention and targeted care. The empirical review of our model shows that it makes diagnoses with a level of accuracy that is unmatched, with a classification efficacy of 98.27%. This shows that the model is good at finding important parts of the brain that may be damaged by cognitive diseases.