An Early Detection and Classification of Alzheimer's Disease Framework Based on ResNet-50

V P Nithya; N Mohanasundaram; R Santhosh

doi:10.2174/1573405620666230825113344

An Early Detection and Classification of Alzheimer's Disease Framework Based on ResNet-50

Curr Med Imaging. 2023 Aug 25. doi: 10.2174/1573405620666230825113344. Online ahead of print.

Authors

V P Nithya¹, N Mohanasundaram², R Santhosh²

Affiliations

¹ Department of Computer Science and Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India.
² Department of Computer Science and Engineering, Faculty 0f Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India.

PMID: 37622561
DOI: 10.2174/1573405620666230825113344

Abstract

Objective: The objective of this study is to develop a more effective early detection system for Alzheimer's disease (AD) using a Deep Residual Network (ResNet) model by addressing the issue of convolutional layers in conventional Convolutional Neural Networks (CNN) and applying image preprocessing techniques.

Methods: The proposed method involves using Contrast Limited Adaptive Histogram Equalizer (CLAHE) and Boosted Anisotropic Diffusion Filters (BADF) for equalization and noise removal and K-means clustering for segmentation. A ResNet-50 model with shortcut links between three residual layers is proposed to extract features more efficiently. ResNet-50 is preferred over other ResNet types due to its intermediate depth, striking a balance between computational efficiency and improved performance, making it a widely adopted and effective architecture for various computer vision tasks. While other ResNet variations may offer higher depths, they are more prone to overfitting and computational complexity, which can hinder their practical application. The proposed method is evaluated on a dataset of MRI scans of AD patients.

Results: The proposed method achieved high accuracy and minimum losses of 95% and 0.12, respectively. While some models showed better accuracy, they were prone to overfitting. In contrast, the suggested framework, based on the ResNet-50 model, demonstrated superior performance in terms of various performance metrics, providing a robust and reliable approach to Alzheimer's disease categorization.

Conclusion: The proposed ResNet-50 model with shortcut links between three residual layers, combined with image preprocessing techniques, provides an effective early detection system for AD. The study demonstrates the potential of deep learning and image processing techniques in developing accurate and efficient diagnostic tools for AD. The proposed method improves the existing approaches to AD classification and provides a promising framework for future research in this area.

Keywords: Alzheimer's disease; Boosted Anisotropic Diffusion Filter; CLAHE; CNN; K-means clustering; ResNet.