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HY-Deepnet : A new Optimal Deep transfer learning empowered framework for an autonomous Alzheimer’s disease detection and diagnosis system

Publication Type : Journal Article

Publisher : Elsevier BV

Source : Engineering Science and Technology, an International Journal

Url : https://doi.org/10.1016/j.jestch.2025.102058

Keywords : COATI optimization, Pre-trained networks, Alzheimer, Deep learning, Feature extraction

Campus : Nagercoil

School : School of Computing

Year : 2025

Abstract : A progressive neurological disorder, Alzheimer’s disease (AD) predominantly impacts memory and cognitive functions of diseased victims. The disease is usually identified by the buildup of abnormal protein deposits in the brain, resulting in the development of plaques and tangles that interfere with communication between nerve cells. Over time, those affected by Alzheimer’s undergo a diminishing mental capacity, affecting their daily activities and ultimately resulting in a loss of independence. While there is currently no remedy to reverse the advancement of Alzheimer’s disease, identifying the initiation of AD can prove highly beneficial within the medical field. This study presents an innovative framework for the detection and diagnosis of Alzheimer’s disease, employing deep transfer models and COATI optimization techniques. The proposed hybrid Deepnet (HY-Deepnet) framework consists of two main phases: detection and diagnosis. The detection phase aims at identifying the presence of disease with the aid of MRI images. This phase evaluates the performance of various deep learning models, including AlexNet, GoogLenet, SqueezeNet, VGGNet, ResNet and explores their combinations. Experimental results reveal that the combination of AlexNet, GoogLenet, and VGGNet outperforms other networks and their combinations, achieving an accuracy of 77.03% without optimization. The second phase focuses on the diagnosis of the detected disease into three different stages. The diagnosis phase is improved from COATI optimization techniques. The proposed HY-Deepnet thus attains an impressive overall accuracy of 97.6%, accompanied by precision, recall, and F1 scores of 0.978, 0.976, and 0.974, respectively. These results underscore the effectiveness of the framework in enhancing Alzheimer’s detection and diagnosis, particularly when leveraging the synergy of deep transfer models and COATI optimization techniques.

Cite this Research Publication : S. Veluchamy, R. Bhuvaneswari, K. Ashwini, Samah Alshathri, Walid El-Shafai, HY-Deepnet : A new Optimal Deep transfer learning empowered framework for an autonomous Alzheimer’s disease detection and diagnosis system, Engineering Science and Technology, an International Journal, Elsevier BV, 2025, https://doi.org/10.1016/j.jestch.2025.102058

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