Evaluation of Artificial Intelligence Accuracy by Increasing the CNN Hidden Layers: Using Cerebral Hemorrhage CT Data

• 발행기관: 한국방사선학회
• 저자명: Han-Jun Kim Min-Ji Kang Eun-Ji Kim Yong-Hyeon Na Jae-Hee Park Su-Eun Baek Su-Man Sim Joo-Wan Hong
• 간행물 정보: 『한국방사선학회 논문지』 제16권 제1호, 1~6쪽, 전체 6쪽
• 주제분류: 공학 > 기타공학
• 파일형태: PDF
• 발행일자: 2022.02.28

영문 초록

Deep learning is a collection of algorithms that enable learning by summarizing the key contents of large amounts of data; it is being developed to diagnose lesions in the medical imaging field. To evaluate the accuracy of the cerebral hemorrhage diagnosis, we used a convolutional neural network (CNN) to derive the diagnostic accuracy of cerebral parenchyma computed tomography (CT) images and the cerebral parenchyma CT images of areas where cerebral hemorrhages are suspected of having occurred. We compared the accuracy of CNN with different numbers of hidden layers and discovered that CNN with more hidden layers resulted in higher accuracy. The analysis results of the derived CT images used in this study to determine the presence of cerebral hemorrhages are expected to be used as foundation data in studies related to the application of artificial intelligence in the medical imaging industry.

목차

Ⅰ. INTRODUCTION

Ⅱ. MATERIAL AND METHODS

Ⅲ. RESULT

Ⅳ. DISCUSSION

Ⅴ. CONCLUSION

Acknowledgement

Reference

키워드

AI CNN hidden layer Computed Tomography Cerebral Hemorrhage