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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 저널정보
- 한국정보통신학회 INTERNATIONAL CONFERENCE ON FUTURE INFORMATION & COMMUNICATION ENGINEERING 2023 INTERNATIONAL CONFERENCE ON FUTURE INFORMATION & COMMUNICATION ENGINEERING Vo.14 No.1
- 발행연도
- 2023.1
- 수록면
- 231 - 236 (6page)
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초록· 키워드
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Alzheimer's disease (AD) is one of the most crucial causes of dementia all over the world, the most common form of dementia, is a complex disease, the mechanisms of which are poorly understood. Parkinson's disease (PD) is a neurodegenerative disorder of the brain. Symptoms of Parkinson's usually appear gradually and progress over time. Both Alzheimer's and Parkinson's disease can disrupt the way the brain processes but are generally considered to be separate and distinct disease entities. However, considerable evidence demonstrates that these disorders share common clinical and neuropathologic features and may present overlapping symptoms, making clinical diagnosis challenging. For example, a significant percentage of AD patients exhibit extrapyramidal features and many PD patients develop dementia. While the exact interplay of the clinical and pathological features between the two diseases is not fully established, there is a definite connection between Alzheimer's and Parkinson's disease. To identify patterns and traits associated with Alzheimer's and Parkinson's, enabling earlier diagnosis and potentially informing future drug discoveries. This study proposes a transfer learning approach where the ImageNet pre-trained convolutional neural network (CNN) models are trained on Parkinson's Progression Markers Initiative (PPMI) dataset to learn a good feature space that may help to classify the deep representation of Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset for early Alzheimer's detection. The model's pre-trained weights of the PPMI dataset have been transferred to a model used for Alzheimer's disease classification. Therefore, the transfer learning approach proposed in this paper can learn the new shared features efficiently. Extensive experiments are conducted to evaluate the performance of the proposal. And this Alzheimer's disease (NC, MCI, LMCI) classification.
목차
Abstract
Ⅰ. INTRODUCTION
Ⅱ. Background and System model
Ⅲ. RESULTS
Ⅳ. DISCUSSION AND CONCLUSIONS
REFERENCES
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