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Prediction of Alzheimer’s Disease Using Adaptive Fine-Tuned Deep Resnet-50 with Attention Mechanism

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Abstract

Alzheimer’s disease (AD) sufferers can benefit from early detection and diagnosis so that protective treatments can be taken before permanent brain impairment develops. Magnetic resonance imaging (MRI)-detected lesion sites in AD patients exhibit significant heterogeneity and these characteristics are spread throughout the imaging space, according to cross-sectional imaging studies of Alzheimer's disease. Because conventional Deep Learning (DL) methods can’t generate enough long-distance correlations in the characteristic space, adaptive fine-tuned deep resnet-50 with attention mechanism (AFDR-AM) is introduced in this research for the early identification of AD. The input MRI data is de-noised using a wavelet thresholding approach, and the contrast is enhanced using Improved Color Histogram Equalization (ICHE). Image recognition and classification characteristics are extracted at the feature extraction phase using a Gabor Filter Bank (GFB). To narrow down a big list of traits, the Artificial Bee Colony Optimization (ABCO) technique can be used to pick the most useful ones and get rid of the rest. Measures of accuracy, precision, recall, f1-score, specificity, and MSE are used to evaluate the effectiveness of the suggested strategy. Comprehensive experiments reveal that our approach achieved the best performance compared to other approaches.

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Data availability

The dataset generated and analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors acknowledged the Ramco Institute of Technology, Rajapalayam, TN, India and Mepco Schlenk Engineering College, Sivakasi, Virudhunagar, Tamilnadu for supporting the research work by providing the facilities.

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Authors and Affiliations

  1. Computer Science and Engineering, Ramco Institute of Technology, Rajapalayam, Virudhunagar, 626117, Tamil Nadu, India

    R. Venkatesh, C. A. Yogaraja & I. Gethzi Ahila Poornima

  2. Artificial Intelligence and Data Science, Mepco Schlenk Engineering College, Sivakasi, Virudhunagar, 626005, Tamilnadu, India

    Shenbagarajan Anantharajan

  3. Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, Virudhunagar, 626005, Tamilnadu, India

    Shenbagalakshmi Gunasekaran

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  1. R. Venkatesh
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  4. C. A. Yogaraja
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  5. I. Gethzi Ahila Poornima
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Correspondence to R. Venkatesh.

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“This article is part of the topical collection “Advances in Computational Approaches for Image Processing, Wireless Networks, Cloud Applications and Network Security” guest edited by P. Raviraj, Maode Ma and Roopashree H R”.

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Venkatesh, R., Anantharajan, S., Gunasekaran, S. et al. Prediction of Alzheimer’s Disease Using Adaptive Fine-Tuned Deep Resnet-50 with Attention Mechanism. SN COMPUT. SCI. 5, 392 (2024). https://doi.org/10.1007/s42979-024-02752-1

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