A Novel AI-Based System for Detection and Severity Prediction of Dementia Using MRI
Dementia is a symptom of Alzheimer’s Disease (A.D.) that affects many people around the globe each year. There is no effective cure to treat this disease, and it can prove to be deadly to the patient if left untreated or undetected. In this paper, the authors propose a novel DCGAN-based A...
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| Autores principales: | , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/737dc31d64f64522a05f283342f7efac |
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| Sumario: | Dementia is a symptom of Alzheimer’s Disease (A.D.) that affects many people around the globe each year. There is no effective cure to treat this disease, and it can prove to be deadly to the patient if left untreated or undetected. In this paper, the authors propose a novel DCGAN-based Augmentation and Classification (D-BAC) model approach to identify and classify dementia into various categories depending upon its prominence and severity in the available MRI scans. The proposed detection of early onset of dementia, also referred to as Mild Cognitive Impairment (MCI), is also studied with the help of a novel GAN-augmented dataset. The proposed model can predict MCI with an accuracy of 74% and can classify dementia into four categories depending upon its prominence in the MRI scan. The authors have also utilized Visual Explainable A.I. (XAI) and have used GradCAM to visually represent the internal working of the model. This novel approach helps verify the differentiating features of the MRI scans learned by the CNN model during training. Three different datasets, namely the original dataset, geometrically transformed images, and a GAN-augmented dataset, have been used for performance analysis. A comparison of the performance of the CNN model has been made on these datasets, and the superiority of results using the novel GAN-augmented dataset has been studied and discussed. Moreover, progressive resizing has also been applied on this GAN-dataset, and different CNN architectures have also been used to achieve better performance scores. The model proposed in the end has a training accuracy of 97% and a testing accuracy of 82% when tested using a conventional CNN architecture and has a testing accuracy of 84% and 87% when tested using VGG-16 and VGG-19 architecture, respectively. |
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