标题
A fuzzy distance-based ensemble of deep models for cervical cancer detection
作者
关键词
Cervical cancer, Computer-aided detection, Deep learning, Fuzzy logic, Ensemble learning
出版物
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
Volume 219, Issue -, Pages 106776
出版商
Elsevier BV
发表日期
2022-03-30
DOI
10.1016/j.cmpb.2022.106776
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- DeepCervix: A deep learning-based framework for the classification of cervical cells using hybrid deep feature fusion techniques
- (2021) Md Mamunur Rahaman et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Cytokine gene variants and socio-demographic characteristics as predictors of cervical cancer: A machine learning approach
- (2021) Manoj Kaushik et al. COMPUTERS IN BIOLOGY AND MEDICINE
- A fuzzy rank-based ensemble of CNN models for classification of cervical cytology
- (2021) Ankur Manna et al. Scientific Reports
- Quantitative detection of cervical cancer based on time series information from smear images
- (2021) C.W. Zhang et al. APPLIED SOFT COMPUTING
- Machine learning-based statistical analysis for early stage detection of cervical cancer
- (2021) Md Mamun Ali et al. COMPUTERS IN BIOLOGY AND MEDICINE
- A novel attention-guided convolutional network for the detection of abnormal cervical cells in cervical cancer screening
- (2021) Lei Cao et al. MEDICAL IMAGE ANALYSIS
- An ensemble of deep transfer learning models for handwritten music symbol recognition
- (2021) Ashis Paul et al. NEURAL COMPUTING & APPLICATIONS
- Exemplar pyramid deep feature extraction based cervical cancer image classification model using pap-smear images
- (2021) Orhan Yaman et al. Biomedical Signal Processing and Control
- Computer-Assisted Screening for Cervical Cancer Using Digital Image Processing of Pap Smear Images
- (2020) Kyi Pyar Win et al. Applied Sciences-Basel
- Inception v3 based cervical cell classification combined with artificially extracted features
- (2020) N. Dong et al. APPLIED SOFT COMPUTING
- Cervical Cell Classification with Graph Convolutional Network
- (2020) Jun Shi et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- Detection of cervical cancer cells based on strong feature CNN-SVM network
- (2020) A. Dongyao Jia et al. NEUROCOMPUTING
- Language identification from multi-lingual scene text images: a CNN based classifier ensemble approach
- (2020) Neelotpal Chakraborty et al. Journal of Ambient Intelligence and Humanized Computing
- A Comprehensive Survey on Transfer Learning
- (2020) Fuzhen Zhuang et al. PROCEEDINGS OF THE IEEE
- Fuzzy Integral-Based CNN Classifier Fusion for 3D Skeleton Action Recognition
- (2020) Avinandan Banerjee et al. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
- Cervical cancer classification using convolutional neural networks and extreme learning machines
- (2019) Ahmed Ghoneim et al. Future Generation Computer Systems-The International Journal of eScience
- Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis
- (2019) Marc Arbyn et al. Lancet Global Health
- Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
- (2018) Freddie Bray et al. CA-A CANCER JOURNAL FOR CLINICIANS
- Handcrafted vs. non-handcrafted features for computer vision classification
- (2017) Loris Nanni et al. PATTERN RECOGNITION
- DeepPap: Deep Convolutional Networks for Cervical Cell Classification
- (2017) Ling Zhang et al. IEEE Journal of Biomedical and Health Informatics
- Cervical Cancer: Prevention and Early Detection
- (2017) Theresa A. Kessler Seminars in Oncology Nursing
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]
- (2010) I Arel et al. IEEE Computational Intelligence Magazine
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationPublish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn More