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Title
Detection of cervical cells based on improved SSD network
Authors
Keywords
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Journal
MULTIMEDIA TOOLS AND APPLICATIONS
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2021-05-25
DOI
10.1007/s11042-021-11015-7
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Related references
Note: Only part of the references are listed.- A generative adversarial network approach to predicting postoperative appearance after orbital decompression surgery for thyroid eye disease
- (2020) Tae Keun Yoo et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Internet of health things-driven deep learning system for detection and classification of cervical cells using transfer learning
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- The Early Detection of Cervical Cancer. The current and changing landscape of cervical disease detection
- (2020) Aslam Shiraz et al. CYTOPATHOLOGY
- Machine learning for assisting cervical cancer diagnosis: An ensemble approach
- (2020) Jiayi Lu et al. Future Generation Computer Systems-The International Journal of eScience
- Inception v3 based cervical cell classification combined with artificially extracted features
- (2020) N. Dong et al. APPLIED SOFT COMPUTING
- Single and Clustered Cervical Cell Classification with Ensemble and Deep Learning Methods
- (2020) Mohammed Kuko et al. INFORMATION SYSTEMS FRONTIERS
- Accuracy and Efficiency of Deep-Learning–Based Automation of Dual Stain Cytology in Cervical Cancer Screening
- (2020) Nicolas Wentzensen et al. JNCI-Journal of the National Cancer Institute
- Detection of cervical cancer cells based on strong feature CNN-SVM network
- (2020) A. Dongyao Jia et al. NEUROCOMPUTING
- Machine Learning Based Classification of Cervical Cancer Using K-Nearest Neighbour, Random Forest and Multilayer Perceptron Algorithms
- (2019) Shakila Basheer et al. Journal of Computational and Theoretical Nanoscience
- Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer
- (2019) Yu-Chun Lin et al. EUROPEAN RADIOLOGY
- Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis
- (2019) Marc Arbyn et al. Lancet Global Health
- A review of image analysis and machine learning techniques for automated cervical cancer screening from pap-smear images
- (2018) Wasswa William et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- Value of [18F]FDG PET radiomic features and VEGF expression in predicting pelvic lymphatic metastasis and their potential relationship in early-stage cervical squamous cell carcinoma
- (2018) Kexin Li et al. EUROPEAN JOURNAL OF RADIOLOGY
- Texture-based feature extraction of smear images for the detection of cervical cancer
- (2018) mithlesh arya et al. IET Computer Vision
- Generative Adversarial Network for Medical Images (MI-GAN)
- (2018) Talha Iqbal et al. JOURNAL OF MEDICAL SYSTEMS
- Multi-feature based benchmark for cervical dysplasia classification evaluation
- (2017) Tao Xu et al. PATTERN RECOGNITION
- A Quantum Hybrid PSO Combined with Fuzzy k-NN Approach to Feature Selection and Cell Classification in Cervical Cancer Detection
- (2017) Abdullah Iliyasu et al. SENSORS
- DeepPap: Deep Convolutional Networks for Cervical Cell Classification
- (2017) Ling Zhang et al. IEEE Journal of Biomedical and Health Informatics
- Deep Convolutional Neural Networks for Hyperspectral Image Classification
- (2015) Wei Hu et al. Journal of Sensors
- An automatic method for robust and fast cell detection in bright field images from high-throughput microscopy
- (2013) Felix Buggenthin et al. BMC BIOINFORMATICS
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