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ORIGINAL RESEARCH article

Front. Med.
Sec. Precision Medicine
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1445325
This article is part of the Research Topic Cluster-based Intelligent Recommendation System for Hybrid Healthcare Units View all 19 articles

Enhancing Healthcare Recommendation: Transfer Learning in Deep Convolutional Neural Networks for Alzheimer Disease Detection

Provisionally accepted
Purushottam K. Pandey Purushottam K. Pandey 1*Jyoti Pruthi Jyoti Pruthi 1Saeed Alzahrani Saeed Alzahrani 2Anshul Verma Anshul Verma 3Benazeer Zohra Benazeer Zohra 4
  • 1 Manav Rachna University, Faridabad, India
  • 2 College of Business Administration, King Saud University, Riyadh, Saudi Arabia
  • 3 Banaras Hindu University, Varanasi, Uttar Pradesh, India
  • 4 Gautam Buddha University, Greater Noida, Uttar Pradesh, India

The final, formatted version of the article will be published soon.

    Neurodegenerative disorders such as Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) significantly impact brain function and cognition. Advanced neuroimaging techniques, particularly Magnetic Resonance Imaging (MRI), play a crucial role in diagnosing these conditions by detecting structural abnormalities. This study leverages the ADNI and OASIS datasets, renowned for their extensive MRI data, to develop effective models for detecting AD and MCI. The research conducted three sets of tests, comparing multiple groups: multi-class classification (AD vs. Cognitively Normal (CN) vs. MCI), binary classification (AD vs. CN, and MCI vs. CN), to evaluate the performance of models trained on ADNI and OASIS datasets. Key preprocessing techniques such as Gaussian filtering, contrast enhancement, and resizing were applied to both datasets. Additionally, skull stripping using U-Net was utilized to extract features by removing the skull. Several prominent deep learning architectures including DenseNet-201, EfficientNet-B0, ResNet-50, ResNet-101, and ResNet-152 were investigated to identify subtle patterns associated with AD and MCI. Transfer learning techniques were employed to enhance model performance, leveraging pre-trained datasets for improved Alzheimer's MCI detection. ResNet-101 exhibited superior performance compared to other models, achieving 98.21% accuracy on the ADNI dataset and 97.45% accuracy on the OASIS dataset in multi-class classification tasks encompassing AD, CN, and MCI. It also performed well in binary classification tasks distinguishing AD from CN. ResNet-152 excelled particularly in binary classification between MCI and CN on the OASIS dataset. These findings underscore the utility of deep learning models in accurately identifying and distinguishing neurodegenerative diseases, showcasing their potential for enhancing clinical diagnosis and treatment monitoring.

    Keywords: deep learning, Densenet, EfficientNet-B0, Resnet, Skull Stripping, Healthcare, Clustering, Decision making

    Received: 07 Jun 2024; Accepted: 20 Aug 2024.

    Copyright: © 2024 Pandey, Pruthi, Alzahrani, Verma and Zohra. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Purushottam K. Pandey, Manav Rachna University, Faridabad, India

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.