Alzheimer’s Disease Classification using Feature Enhanced Deep Convolutional Neural Networks

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Published May 14, 2024
https://doi.org/10.47164/ijngc.v15i1.1242
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Volume 15, Issue 1, March 2024

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R Sreemathy
a:1:{s:5:"en_US";s:37:"Pune Institute of Computer Technology";}
Danish Khan
Pune Institute of Computer Technology
Kisley Chandra
Pune Institute of Computer Technology
Tejas Bora
Pune Institute of Computer Technology
Soumya Khurana
Pune Institute of Computer Technology

Abstract

Neurodegenerative disorders are one of the most insidious disorders that affect millions around the world. Presently, these disorders do not have any remedy, however, if detected at an early stage, therapy can prevent further degeneration. This study aims to detect the early onset of one such neurodegenerative disorder called Alzheimer’s Disease, which is the most prevalent neurological disorder using the proposed Convolutional Neural Network (CNN). These MRI scans are pre-processed by applying various filters, namely, High-Pass Filter, Contrast Stretching, Sharpening Filter, and Anisotropic Diffusion Filter to enhance the Biomarkers in MRI images. A total of 21 models are proposed using different preprocessing and enhancement techniques on transverse and sagittal MRI images. The comparative analysis of the proposed five-layer Convolutional Neural Network (CNN) model with Alex Net is presented. The proposed CNN model outperforms AlexNet and achieves an accuracy of 99.40%, with a precision of 0.988, and recall of 1.00, by using an edge enhanced, contrast stretched, anisotropic diffusion filter. The proposed method may be used to implement automated diagnosis of neurodegenerative disorders.

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite
Sreemathy, R., Khan, D., Chandra, K., Bora, T., & Khurana, S. (2024). Alzheimer’s Disease Classification using Feature Enhanced Deep Convolutional Neural Networks. International Journal of Next-Generation Computing, 15(1). https://doi.org/10.47164/ijngc.v15i1.1242
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