Earticle

초록

영어

Early diagnosis of dementia, such as Alzheimer's disease, is clinically crucial, and deep learning-based brain MRI analysis has gained significant attention. However, acquiring large-scale datasets remains challenging in medical imaging, necessitating learning from small datasets. This study quantitatively compares the dementia classification performance of CNN-based ResNet-18 and various Transformer architectures (Swin Transformer, Vision Transformer, UNETR) using the OASIS-3 brain MRI dataset (3,428 samples) and systematically evaluates their suitability for small-scale medical data. Methods: Data comprising 2,943 normal (85.8%) and 485 dementia (14.2%) cases were split 80:20, with 5-fold cross-validation. Focal Loss was applied to mitigate class imbalance, and metrics including Sensitivity, Specificity, Balanced Accuracy, and AUC-ROC were evaluated. Results: ResNet-18 achieved the most balanced performance with Sensitivity of 79.38%, Balanced Accuracy of 77.38%, and AUC-ROC of 85.40%. Transformer models showed distinctly different patterns: Swin Transformer (Sensitivity 42.27%, AUC 81.78%) exhibited normal-class bias, Vision Transformer (Sensitivity 22.68%, AUC 47.89%) nearly failed to learn with pure global attention, and UNETR (Sensitivity 88.66%, AUC 73.00%) achieved highest sensitivity but severely low specificity (41.77%). ResNet-18 also demonstrated superior learning efficiency (parameters: 33M, training time: 2.1h) compared to Transformers (Swin: 60-70M/3.1h, ViT: 18M/3.0h, UNETR: 10- 12M/4.0h). This study confirms that CNN's inductive bias and structural efficiency are more effective than Transformer's global attention or hybrid approaches in small-scale brain MRI datasets, with ResNet-18 proving most suitable for dementia screening due to balanced sensitivity-specificity trade-off.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
III. A METHOD OF RESEARCH
A. Datasets and preprocessing
B. Model Architecture
C. Learning Settings
D. Evaluation Indicators
IV. THE RESULTS OF AN EXPERIMENT
A. Test Set Performance Comparison
V. DISCUSSION
A. Analysis of Performance Differences
B. Clinical Implications and Limitations
VI. CONCLUSION
REFERENCES

키워드

저자

• Eun-jeong, Kim [ Dept. of Department of Data and Knowledge Service Engineering Graduate school, Dankook University Yongin, South Korea ]
• Eung-Kyo Suh [ Dept. of Department of Data and Knowledge Service Engineering Graduate school, Dankook University Yongin, South Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004