Research Article
Exploring Deep Learning Models for Accurate Alzheimer's Disease Classification based on MRI Imaging
@ARTICLE{10.4108/eetpht.10.5550, author={Irfan Sadiq Rahat and Tuhin Hossain and Hritwik Ghosh and Kamjula Lakshmi Kanth Reddy and Srinivas Kumar Palvadi and J V R Ravindra}, title={Exploring Deep Learning Models for Accurate Alzheimer's Disease Classification based on MRI Imaging}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={10}, number={1}, publisher={EAI}, journal_a={PHAT}, year={2024}, month={3}, keywords={Precise diagnosis, Severity prediction, Patient care, Therapeutic interventions, Magnetic Resonance Imaging, MRI, Traditional Diagnostic methods, Cognitive assessments, Brain Structure}, doi={10.4108/eetpht.10.5550} }- Irfan Sadiq Rahat
Tuhin Hossain
Hritwik Ghosh
Kamjula Lakshmi Kanth Reddy
Srinivas Kumar Palvadi
J V R Ravindra
Year: 2024
Exploring Deep Learning Models for Accurate Alzheimer's Disease Classification based on MRI Imaging
PHAT
EAI
DOI: 10.4108/eetpht.10.5550
Abstract
INTRODUCTION: Alzheimer's disease (AD), a complex neurodegenerative condition, presents significant challenges in early and accurate diagnosis. Early prediction of AD severity holds the potential for improved patient care and timely interventions. This research investigates the use of deep learning methodologies to forecast AD severity utilizing data extracted from Magnetic Resonance Imaging (MRI) scans. OBJECTIVES: This study aims to explore the efficacy of deep learning models in predicting the severity of Alzheimer's disease using MRI data. Traditional diagnostic methods for AD, primarily reliant on cognitive assessments, often lead to late-stage detection. MRI scans offer a non-invasive means to examine brain structure and detect pathological changes associated with AD. However, manual interpretation of these scans is labor-intensive and subject to variability. METHODS: Various deep learning models, including Convolutional Neural Networks (CNNs) and advanced architectures like DenseNet, VGG16, ResNet50, MobileNet, AlexNet, and Xception, are explored for MRI scan analysis. The performance of these models in predicting AD severity is assessed and compared. Deep learning models autonomously learn hierarchical features from the data, potentially recognizing intricate patterns associated with different AD stages that may be overlooked in manual analysis. RESULTS: The study evaluates the performance of different deep learning models in predicting AD severity using MRI scans. The results highlight the efficacy of these models in capturing subtle patterns indicative of AD progression. Moreover, the comparison underscores the strengths and limitations of each model, aiding in the selection of appropriate methodologies for AD prognosis. CONCLUSION: This research contributes to the growing field of AI-driven healthcare by showcasing the potential of deep learning in revolutionizing AD diagnosis and prognosis. The findings emphasize the importance of leveraging advanced technologies, such as deep learning, to enhance the accuracy and timeliness of AD diagnosis. However, challenges remain, including the need for large, annotated datasets, model interpretability, and integration into clinical workflows. Continued efforts in this area hold promise for improving the management of AD and ultimately enhancing patient outcomes.
Copyright © 2024 I. S. Rahat et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
