Early diagnosis of Alzheimer’s disease and mild cognitive impairment using MRI analysis and machine learning algorithms

• Published in: Discover applied sciences Vol. 7; no. 1; pp. 27 - 13
• Main Authors: Givian, Helia, Calbimonte, Jean-Paul
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.01.2025; Springer Nature B.V; Springer
• Subjects: Accuracy; Algorithms; Alzheimer's disease; Alzheimer’s disease (AD); Applied and Technical Physics; Artificial intelligence; Biomarkers; Brain; Brain research; Cerebrospinal fluid; Chemistry/Food Science; Classification; Clinical trials; Cognitive ability; Computer-aided diagnosis (CAD); Datasets; Decision trees; Deep learning; Dementia; Diagnosis; Earth Sciences; Engineering; Environment; Homogeneity; Impairment; Kurtosis; Learning algorithms; Machine learning; Machine learning (ML); Magnetic resonance imaging; Magnetic resonance imaging (MRI); Materials Science; Methods; Mild Cognitive impairment (MCI); Multilayers; Neurodegenerative diseases; Neuroimaging; Statistical analysis; Substantia alba; Substantia grisea; Support vector machines; Texture; Ventricles (cerebral); Magnetic resonance imaging (MRI); Computer-aided diagnosis (CAD); Machine learning (ML); Alzheimer’s disease (AD); Mild Cognitive impairment (MCI)
• ISSN: 3004-9261; 2523-3963; 3004-9261; 2523-3971
• DOI: 10.1007/s42452-024-06440-w

Early diagnosis of Alzheimer’s disease (AD) and mild cognitive impairment (MCI) is crucial to prevent their progression. In this study, we proposed the analysis of magnetic resonance imaging (MRI) based on features including; hippocampus (HC) area size, HC grayscale statistics and texture features (mean, standard deviation, skewness, kurtosis, contrast, correlation, energy, homogeneity, entropy), lateral ventricle (LV) area size, gray matter area size, white matter area size, cerebrospinal fluid area size, patient age, weight, and cognitive score. Five machine learning classifiers; K-nearest neighborhood (KNN), support vector machine (SVM), random forest (RF), decision tree (DT), and multi-layer perception (MLP) were used to distinguish between groups: cognitively normal (CN) vs AD, early MCI (EMCI) vs late MCI (LMCI), CN vs EMCI, CN vs LMCI, AD vs EMCI, and AD vs LMCI. Additionally, the correlation and dependence were calculated to examine the strength and direction of association between each extracted feature and each classification of the group. The average classification accuracies in 20 trials were 95% (SVM), 71.50% (RF), 82.58% (RF), 84.91% (SVM), 85.83% (RF), and 85.08% (RF), respectively, with the best accuracies being 100% (SVM, RF, and MLP), 83.33% (RF), 91.66% (RF), 95% (SVM, and MLP), 96.66% (RF), and 93.33% (DT). Cognitive scores, HC and LV area sizes, and HC texture features demonstrated significant potential for diagnosing AD and its subtypes for all groups. RF and SVM showed better performance in distinguishing between groups. These findings highlight the importance of using 2D-MRI to identify key features containing critical information for early diagnosis of AD. Article Highlights Cognitive scores, brain structure sizes, and tissue features can assist in diagnosing Alzheimer’s and its early stages. Machine learning models classify Alzheimer’s stages using optimized brain MRI features. MRI scans show how brain features change as Alzheimer’s progresses.