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Sengupta, Proshunjeet (2023) Alzheimer disease early-stage diagnosis using Deep Learning methodologies. Masters thesis, Dublin, National College of Ireland.
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Abstract
This research aims to determine the efficiency of learning models in the early detection of Alzheimer’s disease. Early detection plays a vital role since the condition worsens as time goes on. We tested architectures like DenseNet121, InceptionNet, CNN, Attention based CNN and VGG19 using a dataset of medical images that clearly exhibited signs of Alzheimer’s disease. Our findings indicated that DenseNet121 outperformed the models mentioned achieving an accuracy of 81.07%. This suggests that it is more effective in capturing the patterns associated with the Alzheimer’s disease. Interestingly InceptionNet renowned for its optimization of depth and width also yielded results with an accuracy of 54.89%. The success of the attention-based CNN and VGG19 models, which both achieved an accuracy rate of 75% emphasizes the significance of incorporating attention mechanisms and depth in neural network architectures. Conversely the conventional CNN model displayed levels of accuracy with an average of 54.88%. This research significantly contributes to the field by showcasing how various deep learning models can effectively diagnose stages of Alzheimer’s disease.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Milosavljevic, Vladimir UNSPECIFIED |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry R Medicine > Healthcare Industry |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 21 May 2025 11:26 |
| Last Modified: | 21 May 2025 11:26 |
| URI: | https://norma.ncirl.ie/id/eprint/7604 |
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