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
- 학술저널
- 저자정보
- 발행연도
- 2022.8
- 수록면
- 1,233 - 1,241 (9page)
이용수
초록· 키워드
오류제보하기
Tumors of the brain are the deadliest, with a life expectancy of only a few years for those with the most advanced forms. Diagnosing a brain tumor is critical to developing a treatment plan to help patients with the disease live longer. A misdiagnosis of brain tumors will lead to incorrect medical treatment, decreasing a patient"s chance of survival. Radiologists classify brain tumors via biopsy, which takes a long time. As a result, the doctor will need an automatic classification system to identify brain tumors. Image classification is one application of the deep learning method in computer vision. One of the deep learning"s most powerful algorithms is the convolutional neural network (CNN). This paper will introduce a novel deep learning structure and image gradient to classify brain tumors. Meningioma, glioma, and pituitary tumors are the three most popular forms of brain cancer represented in the Figshare dataset, which contains 3,064 T1-weighted brain images from 233 patients. According to the numerical results, our method is more accurate than other approaches.
목차
ABSTRACT
1. INTRODUCTION
2. RELATED WORKS
3. METHODOLOGY
4. EXPERIMENTAL RESULTS
5. CONCLUSION
REFERENCE
참고문헌 (20)
참고문헌 신청
Classification of Brain Tumors, https://www.aans.org/en/Media/Classifications-of-Brain-Tumors (Accessed on 8 June 2022).
A. Rehman, S. Naz, M.I. Razzak, F. Akram, and M. Imran, “A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning,” Circuits, Systems, and Signal Processing, Vol. 39, No. 2, pp. 757-775, 2019.
A. Pashaei, H. Sajedi, and N. Jazayeri, “Brain Tumor Classification via Convolutional Neural Network and Extreme Learning Machines,” 8th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 314-319, 2018.
A. Lakshmi and T. Arivoli, “Computer Aided Diagnosis System for Brain Tumor Detection And Segmentation,” Journal of Theoretical & Applied Information Technology, Vol. 64, No. 2, pp. 561-597, 2014.
R. Nalbalwar, U. Majhi, R. Patil, and S. Gonge, “Detection of Brain Tumor by Using ANN,” International Journal of Research in Advent Technology, Vol. 2, No. 4, pp. 279-282, 2014.
A. Pashaei, H. Sajedi, and N. Jazayeri, “Brain Tumor Classification via Convolutional Neural Network and Extreme Learning Machines,” 8th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 314-319, 2018.
A. Lakshmi and T. Arivoli, “Computer Aided Diagnosis System for Brain Tumor Detection And Segmentation,” Journal of Theoretical & Applied Information Technology, Vol. 64, No. 2, pp. 561-597, 2014.
R. Nalbalwar, U. Majhi, R. Patil, and S. Gonge, “Detection of Brain Tumor by Using ANN,” International Journal of Research in Advent Technology, Vol. 2, No. 4, pp. 279-282, 2014.
함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
Enhancing MRI- Based Brain Tumor Diagnosis with Deep Learning & Data Augmentation
한국정보통신학회 종합학술대회 논문집
2024 .10
-
향상된 Mask R-CNN을 적용한 뇌종양 검출
대한전자공학회 학술대회
2019 .11
-
혈액-뇌종양 장벽 모사를 위한 3 차원 뇌종양 스페로이드 결합 미세유체 플랫폼
대한기계학회 춘추학술대회
2021 .04
-
“뇌 모델링 기술과 BCI” 특집을 내면서
정보과학회지
2020 .10
-
형태학적 연산과 뇌종양 평균 크기를 이용한 감마나이프 치료 범위 자동 검출 알고리
멀티미디어학회논문지
2015 .10
-
In Vitro Reconstruction of Brain Tumor Microenvironment
BioChip Journal
2019 .01
-
Brain-on-a-chip Models for Studying Brain Diseases
한국고분자학회 학술대회 연구논문 초록집
2023 .04
-
Brain Tumor Segmentation Study Through U-Net Architecture Modification
대한전자공학회 학술대회
2021 .11
-
Performance Analysis of Classification Techniques of Human Brain MRI Images
INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS
2019 .12
-
Brain-on-a-Chip Technology for Mimicking Brain Microvasculature Physiology and Patholog
한국고분자학회 학술대회 연구논문 초록집
2021 .10
-
엣지에 민감한 합성곱 신경망를 이용한 뇌 네트워크 분해 기법
대한전자공학회 학술대회
2018 .06
-
A Blockchain-Based Framework for Distributing and Managing CNN-Derived Brain Tumor Detection Models
한국통신학회 학술대회논문집
2024 .06
-
딥러닝을 이용한 뇌종양 진단 기술
한국통신학회 학술대회논문집
2018 .06
-
Brain-Inspired 인공지능과 뇌공학
정보과학회지
2018 .01
-
Superenhancers as master gene regulators and novel therapeutic targets in brain tumors
Experimental and Molecular Medicine
2023 .02
-
MR영상을 사용하여 뇌종양을 세분화 하기위한 딥러닝 모델 개발
정보 및 제어 논문집
2021 .04
-
Brain-on-a-chip for disease modelling
한국생물공학회 학술대회
2021 .10
-
Flexible, Sticky, and Biodegradable Wireless Device for Drug Delivery to Brain Tumors
한국생물공학회 학술대회
2022 .09
-
Evaluation of Deep Learning Based YOLOv3 Brain Tumor Classification Performance Using Magnetic Resonance Imaging
대한자기공명기술학회지
2021 .01
-
딥러닝 기반 특징 앙상블을 사용한 MRI 영상의 뇌종양 분류
한국컴퓨터정보학회논문지
2021 .07
이 논문의 저자 정보
최근 본 자료
댓글(0)
0
UCI(KEPA) : I410-ECN-0101-2022-004-001691454