ParkinNet: a Novel Approach to Classifying Alzheimer's and Parkinson's Diseases Using Brain Structural MRI
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| Publicat a: | ADCAIJ : Advances in Distributed Computing and Artificial Intelligence Journal vol. 14 (2025), p. e32178-e32194 |
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| Autor principal: | |
| Altres autors: | , , , , , |
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Ediciones Universidad de Salamanca
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| Accés en línia: | Citation/Abstract Full Text - PDF |
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| 001 | 3282913960 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2255-2863 | ||
| 024 | 7 | |a 10.14201/adcaij.32178 |2 doi | |
| 035 | |a 3282913960 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Ali, Md Asraf | |
| 245 | 1 | |a ParkinNet: a Novel Approach to Classifying Alzheimer's and Parkinson's Diseases Using Brain Structural MRI | |
| 260 | |b Ediciones Universidad de Salamanca |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Both Parkinson's disease (PD) and Alzheimer's disease (AD) are forms of neurodegeneration, which are linked to the same biochemical alterations in the brain. The mixed pathology of these diseases may cause diagnostic dilemmas, which may lead to misdiagnosis. Because of this, classification of AD and PD is essential to reduce extra healthcare costs and the patients' stress. However, the classification of AD and PD can be challenging because of the overlapping symptoms and risk factors. Therefore, the purpose of this study is to develop a model named ParkinNet to classify AD and PD. The current study used Global Average Pooling and Adam optimiser with a batch size of 64. For evaluation, seven deep learning algorithms are used, including MobileNetV2, EfficientNetB2, InceptionResNetv2, VGG16, VGG19, InceptionV3 and ResNet50, along with the proposed ParkinNet model. The proposed ParkinNet model outperforms the other existing models examined in this study and yields an accuracy of 98. 54 %. The precise classification of these diseases may contribute to the diagnosis process of AD and PD. | |
| 653 | |a Brain | ||
| 653 | |a Alzheimer's disease | ||
| 653 | |a Classification | ||
| 653 | |a Machine learning | ||
| 653 | |a Parkinson's disease | ||
| 700 | 1 | |a Ahammad, Mejbah | |
| 700 | 1 | |a Nawshad, Nadim | |
| 700 | 1 | |a Sirajum, Munira Shifat | |
| 700 | 1 | |a M. Firoz Mridha | |
| 700 | 1 | |a Ahmmed, Faysal | |
| 700 | 1 | |a Noor A Jannat Tania | |
| 773 | 0 | |t ADCAIJ : Advances in Distributed Computing and Artificial Intelligence Journal |g vol. 14 (2025), p. e32178-e32194 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3282913960/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3282913960/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |