지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2023.10
- 수록면
- 1,737 - 1,740 (4page)
이용수
이 논문의 연구 히스토리 (2)
- 이 논문의 후속연구가 궁금하신가요?
- 연관 학술논문 또는 학술발표를 통해 보다 발전된 연구결과를 확인하실 수 있습니다.
- 이 논문의 연구 히스토리 확인하기
초록· 키워드
오류제보하기
Radiation is widely used in medicine to diagnose and treat disease. CT (Computed Tomography) scans allow early detection of externally invisible diseases and appropriate treatment. However, radiation exposure from the examination may result in a future risk of cancer. Efforts are therefore being made to reduce radiation exposure. During the examination, noise is generated in the image when the dose is reduced. Noise reduces the visibility of the image and may cause lesions to be missed. In this study, we focus on Convolutional Neural Networks (CNNs), a type of deep learning model that has recorded high accuracy in image processing tasks. The proportion of frequency components in
an image has more low-frequency components and fewer high-frequency components. Since image features are treated equally across channels, important information such as noise and edges are easily lost. To solve this problem, we propose CNN with channel attention module. In addition, we employ MAE as the loss function to enable effective learning. Using whole body slice CT images of pigs, we evaluate the image quality by Peak Signal-to-Noise Ratio (PSNR) and show that the proposed method is effective.
an image has more low-frequency components and fewer high-frequency components. Since image features are treated equally across channels, important information such as noise and edges are easily lost. To solve this problem, we propose CNN with channel attention module. In addition, we employ MAE as the loss function to enable effective learning. Using whole body slice CT images of pigs, we evaluate the image quality by Peak Signal-to-Noise Ratio (PSNR) and show that the proposed method is effective.
목차
Abstract
1. INTRODUCTION
2 METHODS
4. EXPERIMENT AND RESULTS
4. DISCUSSION
5. CONCLUSION
REFERENCES
참고문헌 (0)
참고문헌 신청함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
Denoising on Low-Dose CT Image Using Deep CNN
제어로봇시스템학회 국제학술대회 논문집
2022 .11
-
A Method for Denoising from Low-Dose CT images Based on Noise2Noise
제어로봇시스템학회 국제학술대회 논문집
2023 .10
-
RED-CNN에 기반한 CT 영상 픽셀 크기에 따른 denoising 성능평가
대한전자공학회 학술대회
2019 .11
-
Image Denoising with a Convolution Neural Network using Gaussian Filtered Residuals
IEIE Transactions on Smart Processing & Computing
2021 .04
-
RED-CNN에 기반한 X-ray dose에 따른 CT 영상에서의 denoising 평가
대한전자공학회 학술대회
2019 .06
-
Development of a Place Image Analysis Algorithm through Image Crawling and Deep-Learning
한국통신학회 학술대회논문집
2020 .11
-
Enhancing Medical Images by New Fuzzy Membership Function Median Based Noise Detection and Filtering Technique
Journal of Electrical Engineering & Technology
2015 .09
-
Convolutional Neural Network Based Image Processing System
Journal of information and communication convergence engineering
2018 .09
-
Performance Analysis of Denoising Algorithms for Human Brain Image
INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS
2018 .09
-
구조해석을 위한 넙다리뼈의 CT 영상 추출 과정에 대한 연구
한국기계가공학회 춘추계학술대회 논문집
2019 .10
-
CT 영상의 화질개선을 위한 이중트리복합웨이블릿의 적용
한국방사선학회 논문지
2019 .01
-
잡음제거 합성곱 신경망을 이용한 이미지 복원 방법
멀티미디어학회논문지
2022 .01
-
Convolutional LSTM 모델을 이용한 인장편 특성 추정 방법
한국컴퓨터정보학회논문지
2018 .11
-
고해상도 이미지의 실시간 Edge-Detecting 및 De-Noising 방법
한국항공우주학회 학술발표회 초록집
2024 .11
-
3D Non-Rigid Registration for Abdominal PET-CT and MR Images Using Mutual Information and Independent Component Analysis
IEIE Transactions on Smart Processing & Computing
2015 .10
-
자동표적인식 성능 향상을 위한 전자광학/적외선 영상 잡음 판별 기법
제어로봇시스템학회 논문지
2017 .02
-
A study on image area analysis and improvement using denoising technique
전기전자학회논문지
2021 .09
-
에지 분류 CNN 을 이용한 U-Net 기반 에지 검출
제어로봇시스템학회 논문지
2019 .08
-
A Noisy Infrared and Visible Light Image Fusion Algorithm
JIPS(Journal of Information Processing Systems)
2021 .10
-
CT 촬영 조건에 따른 PET 영상의 변화
한국방사선학회 논문지
2019 .01
이 논문의 저자 정보
이 논문과 함께 이용한 논문
최근 본 자료
댓글(0)
0
UCI(KEPA) : I410-151-24-02-088266400