HY-Deepnet : A new Optimal Deep transfer learning empowered framework for an autonomous Alzheimer’s disease detection and diagnosis system
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...
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Elsevier
2025-08-01
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| Series: | Engineering Science and Technology, an International Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215098625001132 |
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| author | S. Veluchamy R. Bhuvaneswari K. Ashwini Samah Alshathri Walid El-Shafai |
| author_facet | S. Veluchamy R. Bhuvaneswari K. Ashwini Samah Alshathri Walid El-Shafai |
| author_sort | S. Veluchamy |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8325ff489d224518b70d8ec35ba3f8ea |
| institution | DOAJ |
| issn | 2215-0986 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering Science and Technology, an International Journal |
| spelling | doaj-art-8325ff489d224518b70d8ec35ba3f8ea2025-08-20T03:13:32ZengElsevierEngineering Science and Technology, an International Journal2215-09862025-08-016810205810.1016/j.jestch.2025.102058HY-Deepnet : A new Optimal Deep transfer learning empowered framework for an autonomous Alzheimer’s disease detection and diagnosis systemS. Veluchamy0R. Bhuvaneswari1K. Ashwini2Samah Alshathri3Walid El-Shafai4Department of Electronics and Communication Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Nagercoil, IndiaDepartment of Computer Science and Engineering, Rajalakshmi Engineering College, Thandalam, Chennai, India; Corresponding author at: Rajalakshmi Engineering College, Thandalam, Chennai, India.Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, IndiaDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaAutomated Systems and Computing Lab (ASCL), Computer Science Department, Prince Sultan University, Riyadh 11586, Saudi Arabia; Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, EgyptA 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.http://www.sciencedirect.com/science/article/pii/S2215098625001132COATI optimizationPre-trained networksAlzheimerDeep learningFeature extraction |
| spellingShingle | 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 COATI optimization Pre-trained networks Alzheimer Deep learning Feature extraction |
| title | HY-Deepnet : A new Optimal Deep transfer learning empowered framework for an autonomous Alzheimer’s disease detection and diagnosis system |
| title_full | HY-Deepnet : A new Optimal Deep transfer learning empowered framework for an autonomous Alzheimer’s disease detection and diagnosis system |
| title_fullStr | HY-Deepnet : A new Optimal Deep transfer learning empowered framework for an autonomous Alzheimer’s disease detection and diagnosis system |
| title_full_unstemmed | HY-Deepnet : A new Optimal Deep transfer learning empowered framework for an autonomous Alzheimer’s disease detection and diagnosis system |
| title_short | HY-Deepnet : A new Optimal Deep transfer learning empowered framework for an autonomous Alzheimer’s disease detection and diagnosis system |
| title_sort | hy deepnet a new optimal deep transfer learning empowered framework for an autonomous alzheimer s disease detection and diagnosis system |
| topic | COATI optimization Pre-trained networks Alzheimer Deep learning Feature extraction |
| url | http://www.sciencedirect.com/science/article/pii/S2215098625001132 |
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