Literature DB >> 8168062

Computer-assisted cervical cancer screening using neural networks.

L J Mango1.   

Abstract

A practical and effective system for the computer-assisted screening of conventionally prepared cervical smears is presented and described. Recent developments in neural network technology have made computerized analysis of the complex cellular scenes found on Pap smears possible. The PAPNET Cytological Screening System uses neural networks to automatically analyze conventional smears by locating and recognizing potentially abnormal cells. It then displays images of these objects for review and final diagnosis by qualified cytologists. The results of the studies presented indicate that the PAPNET system could be a useful tool for both the screening and rescreening of cervical smears. In addition, the system has been shown to be sensitive to some types of abnormalities which have gone undetected during manual screening.

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Year:  1994        PMID: 8168062     DOI: 10.1016/0304-3835(94)90098-1

Source DB:  PubMed          Journal:  Cancer Lett        ISSN: 0304-3835            Impact factor:   8.679


  10 in total

1.  Origins of ... image analysis in clinical pathology.

Authors:  G A Meijer; J A Beliën; P J van Diest; J P Baak
Journal:  J Clin Pathol       Date:  1997-05       Impact factor: 3.411

2.  Quantitative automated image analysis system with automated debris filtering for the detection of breast carcinoma cells.

Authors:  David T Martin; Sergio Sandoval; Casey N Ta; Manuel E Ruidiaz; Maria Jose Cortes-Mateos; Davorka Messmer; Andrew C Kummel; Sarah L Blair; Jessica Wang-Rodriguez
Journal:  Acta Cytol       Date:  2011-04-27       Impact factor: 2.319

3.  Evolution of dental informatics as a major research tool in oral pathology.

Authors:  Sasidhar Singaraju; H Prasad; Medhini Singaraju
Journal:  J Oral Maxillofac Pathol       Date:  2012-01

4.  Automated Cell Selection Using Support Vector Machine for Application to Spectral Nanocytology.

Authors:  Qin Miao; Justin Derbas; Aya Eid; Hariharan Subramanian; Vadim Backman
Journal:  Biomed Res Int       Date:  2016-01-19       Impact factor: 3.411

5.  Performance of A Convolutional Neural Network in Screening Liquid Based Cervical Cytology Smears.

Authors:  Parikshit Sanyal; Sanghita Barui; Prabal Deb; Harish Chander Sharma
Journal:  J Cytol       Date:  2019 Jul-Sep       Impact factor: 1.000

Review 6.  A Review of Computational Methods for Cervical Cells Segmentation and Abnormality Classification.

Authors:  Teresa Conceição; Cristiana Braga; Luís Rosado; Maria João M Vasconcelos
Journal:  Int J Mol Sci       Date:  2019-10-15       Impact factor: 5.923

7.  Identification of women with high grade histopathology results after conisation by artificial neural networks.

Authors:  Marko Mlinaric; Miljenko Krizmaric; Iztok Takac; Alenka Repse Fokter
Journal:  Radiol Oncol       Date:  2022-08-14       Impact factor: 4.214

8.  Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears.

Authors:  Xiaohui Zhu; Xiaoming Li; Kokhaur Ong; Wenli Zhang; Wencai Li; Longjie Li; David Young; Yongjian Su; Bin Shang; Linggan Peng; Wei Xiong; Yunke Liu; Wenting Liao; Jingjing Xu; Feifei Wang; Qing Liao; Shengnan Li; Minmin Liao; Yu Li; Linshang Rao; Jinquan Lin; Jianyuan Shi; Zejun You; Wenlong Zhong; Xinrong Liang; Hao Han; Yan Zhang; Na Tang; Aixia Hu; Hongyi Gao; Zhiqiang Cheng; Li Liang; Weimiao Yu; Yanqing Ding
Journal:  Nat Commun       Date:  2021-06-10       Impact factor: 14.919

Review 9.  Screening for cervical cancer using automated analysis of PAP-smears.

Authors:  Ewert Bengtsson; Patrik Malm
Journal:  Comput Math Methods Med       Date:  2014-03-20       Impact factor: 2.238

10.  Detection of leukocoria using a soft fusion of expert classifiers under non-clinical settings.

Authors:  Pablo Rivas-Perea; Erich Baker; Greg Hamerly; Bryan F Shaw
Journal:  BMC Ophthalmol       Date:  2014-09-09       Impact factor: 2.209

  10 in total

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