Transfer learning in severity classification in Alzheimer's : A benchmark comparative study on deep neural networks
| dc.authorid | 0000-0003-2415-9131 | |
| dc.authorid | 0000-0003-2276-2658 | |
| dc.contributor.author | Kırtay, Seda | |
| dc.contributor.author | Koçak, Muhammed Tayyip | |
| dc.date.accessioned | 2024-08-02T12:53:56Z | |
| dc.date.available | 2024-08-02T12:53:56Z | |
| dc.date.issued | 2024 | |
| dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümü | |
| dc.description.abstract | Alzheimer's disease has become a condition of the brain that progresses over time and impacts a significant number of individuals worldwide. Early diagnosis, timely intervention and management of this disease process are very important in Alzheimer's disease. With regard to this study, we propose a transfer learning based early detection approach for Alzheimer's disease using Moderate Demented, Mild Demented, No Demented and Very Mild Demented classification sets. The proposed approach utilizes transfer learning based on the use of a deep neural network model that has been trained to extract features from brain imaging data. To evaluate the performance in transfer learning, a dataset of 6,400 images from brain MRI scans is augmented using data augmentation techniques and used in various convolutional neural network models the like VGG-19, Resnet-50, DenseNet-121, Inception-V3, VGG-16. The results are planned to show that these models achieve high sensitivity, specificity and high accuracy in detecting early signs of Alzheimer's disease. The study also emphasizes these advantages of using transfer methods of learning for early Alzheimer's detection by comparing it with various other deep learning models. The findings of this research suggest that transfer learning-based approaches can aid in the early detection of Alzheimer's disease., which affects millions of people, and offer a practical solution to classify cognitive impairment. With the proposed approach, it is shown that by helping clinicians to detect individuals at risk of Alzheimer's at an early stage, it will be possible to provide timely intervention and, in fact, better patient care. In terms of more effective applicability in clinical applications, the proposed approach can be applied to different and larger datasets and populations to make improvements and provide convenience to clinicians and patients. The best success rate of the models we used is achieved on the VGG19, RESNET50 KNN model with 99 percent. | |
| dc.identifier.citation | Kırtay, S., & Koçak, M. T. (2024). Transfer learning in severity classification in Alzheimer's : A benchmark comparative study on deep neural networks. Aurum Journal of Health Sciences, 6(2), 91-108. | |
| dc.identifier.endpage | 108 | |
| dc.identifier.issn | 2651-2815 | |
| dc.identifier.issue | 2 | |
| dc.identifier.startpage | 91 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.13055/744 | |
| dc.identifier.volume | 6 | |
| dc.institutionauthor | Koçak, Muhammed Tayyip | |
| dc.institutionauthorid | 0000-0003-2276-2658 | |
| dc.language.iso | tr | |
| dc.publisher | Altınbas University | |
| dc.relation.ispartof | Aurum Journal of Health Sciences | |
| dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | Deep Learning | |
| dc.subject | Transfer Learning | |
| dc.subject | Alzheimer | |
| dc.subject | Disease Classification | |
| dc.subject | Machine Learning | |
| dc.title | Transfer learning in severity classification in Alzheimer's : A benchmark comparative study on deep neural networks | |
| dc.type | Article | |
| dspace.entity.type | Publication |
