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A Hybrid Deep Learning Framework to Predict Alzheimer’s Disease Progression Using Generative Adversarial Networks and Deep Convolutional Neural Networks

  • Research Article-Computer Engineering and Computer Science
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Abstract

A major research subject in recent times is Alzheimer’s disease (AD) due to the growth and considerable societal impacts on health. So, the detection of AD is essential for medication care. Early detection of AD is critical for effective treatment, and monitoring the time period between normal aging’s unavoidable cognitive loss and dementia’s more catastrophic degradation is common practice. The deep learning method for early diagnosis and automated categorization of AD has suddenly gained a lot of attention since rapid advancement in the field of GANs approaches has now been used in the clinical research sector. Many recent studies using brain MRI images and convolutional neural networks (CNNs) to identify Alzheimer’s disease have yielded promising results. Instead of adequately engaging with the lack of real data, many research papers have focused on prediction. The main purpose of this paper is to do this by generating synthetic MRI images using a series of DCGANs. This paper demonstrates the effectiveness of this concept by cascading DCGANs that imitate different stages of Alzheimer’s disease and utilizing SRGANs to enhance the resolution of MRI scans. The purpose of this research is to come forward and tell if an individual might just get Alzheimer’s disease. CNN, DCGANs, and SRGANs are used in this paper to present a deep learning-based approach that improves classification and prediction accuracy to 99.7% and also handles the lack of data and the resolution of data.

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Acknowledgements

We sincerely acknowledge the editor as well as the reviewers for their insightful comments that helped to enhance the manuscript. The Alzheimer’s Disease Neuroimaging Initiative shared the data for this investigation.

Funding

This research was self-funded.

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Authors and Affiliations

  1. Department of Computer Science, St. Xavier’s College (Autonomous), Kolkata, West Bengal, 700016, India

    Rajarshi SinhaRoy

  2. Department of Computer Science, Government General Degree College, Singur, West Bengal, 712409, India

    Anupam Sen

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RS led the implementations, data preprocessing, formal analysis, and experiments, wrote the original manuscript, and revised the manuscript. AS supervised and managed the research. RS contributed to the article and approved the submitted version.

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Correspondence to Rajarshi SinhaRoy.

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The authors declared that they have no conflicts of interest in this research. The authors certify that they are free of any known financial conflicts of interest or close personal ties that might have looked to have affected the research presented in this study.

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SinhaRoy, R., Sen, A. A Hybrid Deep Learning Framework to Predict Alzheimer’s Disease Progression Using Generative Adversarial Networks and Deep Convolutional Neural Networks. Arab J Sci Eng 49, 3267–3284 (2024). https://doi.org/10.1007/s13369-023-07973-9

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