A novel CNN architecture for accurate early detection and classification of Alzheimer’s disease using MRI data

• Published in: Scientific reports Vol. 14; no. 1; p. 3463
• Main Authors: El-Assy, A. M., Amer, Hanan M., Ibrahim, H. M., Mohamed, M. A.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12.02.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/378; 631/61; 639/166; 692/308; 692/700; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Classification; Convolutional neural network; Deep learning; Explain ability; Humanities and Social Sciences; Humans; Intelligent systems; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical personnel; multidisciplinary; Neural networks; Neural Networks, Computer; Neurodegenerative diseases; Neuroimaging; Neuroimaging - methods; Prognosis; Science; Science (multidisciplinary); Deep learning; Alzheimer’s disease; Explain ability; Intelligent systems; Convolutional neural network
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-53733-6

Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder that requires accurate diagnosis for effective management and treatment. In this article, we propose an architecture for a convolutional neural network (CNN) that utilizes magnetic resonance imaging (MRI) data from the Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset to categorize AD. The network employs two separate CNN models, each with distinct filter sizes and pooling layers, which are concatenated in a classification layer. The multi-class problem is addressed across three, four, and five categories. The proposed CNN architecture achieves exceptional accuracies of 99.43%, 99.57%, and 99.13%, respectively. These high accuracies demonstrate the efficacy of the network in capturing and discerning relevant features from MRI images, enabling precise classification of AD subtypes and stages. The network architecture leverages the hierarchical nature of convolutional layers, pooling layers, and fully connected layers to extract both local and global patterns from the data, facilitating accurate discrimination between different AD categories. Accurate classification of AD carries significant clinical implications, including early detection, personalized treatment planning, disease monitoring, and prognostic assessment. The reported accuracy underscores the potential of the proposed CNN architecture to assist medical professionals and researchers in making precise and informed judgments regarding AD patients.