Open Access

ARTICLE

Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

Atif Mehmood^1,2, Zhonglong Zheng^1,*, Rizwan Khan¹, Ahmad Al Smadi³, Farah Shahid^1,2, Shahid Iqbal⁴, Mutasem K. Alsmadi⁵, Yazeed Yasin Ghadi⁶, Syed Aziz Shah⁸, Mostafa M. Ibrahim⁷

1 Department of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321002, China
2 Zhejiang Institute of Photoelectronics & Zhejiang Institute for Advanced Light Source, Zhejiang Normal University, Jinhua, 321004, China
3 Department of Data Science and Artificial Intelligence, Zarqa University, Zarqa, 13100, Jordan
4 School of Chemical and Environmental Engineering, College of Chemistry, Chemical Engineering and Materials Science, Soochow University, Suzhou, 215123, China
5 Department of Management Information Systems, College of Applied Studies and Community Service, Imam Abdulrahman Bin Faisal University, Dammam, 32244, Saudi Arabia
6 Department of Computer Science, Al Ain University, Abu Dhabi, 999041, United Arab Emirates
7 Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia, 61519, Egypt
8 Healthcare Technology and Innovation Theme, Faculty Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV4 8BW, UK

* Corresponding Author: Zhonglong Zheng. Email:

Computers, Materials & Continua 2024, 80(2), 2081-2098. https://doi.org/10.32604/cmc.2024.046869

Received 07 October 2023; Accepted 26 March 2024; Issue published 15 August 2024

Abstract

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease (AD). Mild cognitive impairment (MCI) is a condition that falls between the spectrum of normal cognitive function and AD. However, previous studies have mainly used handcrafted features to classify MCI, AD, and normal control (NC) individuals. This paper focuses on using gray matter (GM) scans obtained through magnetic resonance imaging (MRI) for the diagnosis of individuals with MCI, AD, and NC. To improve classification performance, we developed two transfer learning strategies with data augmentation (i.e., shear range, rotation, zoom range, channel shift). The first approach is a deep Siamese network (DSN), and the second approach involves using a cross-domain strategy with customized VGG-16. We performed experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset to evaluate the performance of our proposed models. Our experimental results demonstrate superior performance in classifying the three binary classification tasks: NC vs. AD, NC vs. MCI, and MCI vs. AD. Specifically, we achieved a classification accuracy of 97.68%, 94.25%, and 92.18% for the three cases, respectively. Our study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI, AD, and normal control individuals using GM scans. Our findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

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Keywords

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