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Pap smear image classification using convolutional neural network

Authors:

Manish Chowdhury,

Lipi B. Mahanta,

Malay K. Kundu,

Anup K. DasAuthors Info & Claims

ICVGIP '16: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing

Article No.: 55, Pages 1 - 8

https://doi.org/10.1145/3009977.3010068

Published: 18 December 2016 Publication History

Abstract

This article presents the result of a comprehensive study on deep learning based Computer Aided Diagnostic techniques for classification of cervical dysplasia using Pap smear images. All the experiments are performed on a real indigenous image database containing 1611 images, generated at two diagnostic centres. Focus is given on constructing an effective feature vector which can perform multiple level of representation of the features hidden in a Pap smear image. For this purpose Deep Convolutional Neural Network is used, followed by feature selection using an unsupervised technique with Maximal Information Compression Index as similarity measure. Finally performance of two classifiers namely Least Square Support Vector Machine (LSSVM) and Softmax Regression are monitored and classifier selection is performed based on five measures along with five fold cross validation technique. Output classes reflects the established Bethesda system of classification for identifying pre-cancerous and cancerous lesion of cervix. The proposed system is also compared with two existing conventional systems and also tested on a publicly available database. Experimental results and comparison shows that proposed system performs efficiently in Pap smear classification.

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Cited By

Man XSong Y(2024)Classification of Biomedical Images with Mined Statistical Features and Dynamic ProgrammingCurrent Computer Science10.2174/012950377929101224042407035703Online publication date: 22-Oct-2024
https://doi.org/10.2174/0129503779291012240424070357
Cai DChen JZhao JXue YYang SYuan WFeng MWeng HLiu SPeng YZhu JWang KJackson CTang HHuang JWang X(2024)HiCervix: An Extensive Hierarchical Dataset and Benchmark for Cervical Cytology ClassificationIEEE Transactions on Medical Imaging10.1109/TMI.2024.341969743:12(4344-4355)Online publication date: Dec-2024
https://doi.org/10.1109/TMI.2024.3419697
Prome TJasim Tahiti T(2024)MobileNetV2 Based Cervical Cancer Classification Using Pap Smear Images2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)10.1109/ACDSA59508.2024.10467627(1-7)Online publication date: 1-Feb-2024
https://doi.org/10.1109/ACDSA59508.2024.10467627
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Pap smear image classification using convolutional neural network
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Published In

cover image ACM Other conferences

ICVGIP '16: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing

December 2016

743 pages

ISBN:9781450347532

DOI:10.1145/3009977

Program Chairs:
Dhruv Batra
Georgia Tech
,
Michael Brown
York University, Canada
,
Vijay Natarajan
Indian Institute of Science, Bangalore

Copyright © 2016 ACM.

© 2016 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Google Inc.
QI: Qualcomm Inc.
Tata Consultancy Services
NVIDIA
MathWorks: The MathWorks, Inc.
Microsoft Research: Microsoft Research

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 December 2016

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MathWorks
Microsoft Research

ICVGIP '16: Indian Conference on Computer Vision, Graphics and Image Processing

December 18 - 22, 2016

Assam, Guwahati, India

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ICVGIP '16 Paper Acceptance Rate 95 of 286 submissions, 33%;

Overall Acceptance Rate 95 of 286 submissions, 33%

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Cited By

Man XSong Y(2024)Classification of Biomedical Images with Mined Statistical Features and Dynamic ProgrammingCurrent Computer Science10.2174/012950377929101224042407035703Online publication date: 22-Oct-2024
https://doi.org/10.2174/0129503779291012240424070357
Cai DChen JZhao JXue YYang SYuan WFeng MWeng HLiu SPeng YZhu JWang KJackson CTang HHuang JWang X(2024)HiCervix: An Extensive Hierarchical Dataset and Benchmark for Cervical Cytology ClassificationIEEE Transactions on Medical Imaging10.1109/TMI.2024.341969743:12(4344-4355)Online publication date: Dec-2024
https://doi.org/10.1109/TMI.2024.3419697
Prome TJasim Tahiti T(2024)MobileNetV2 Based Cervical Cancer Classification Using Pap Smear Images2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)10.1109/ACDSA59508.2024.10467627(1-7)Online publication date: 1-Feb-2024
https://doi.org/10.1109/ACDSA59508.2024.10467627
Welch ELu CSung CZhang CTripathi AOu J(2024)BMT: A Cross-Validated ThinPrep Pap Cervical Cytology Dataset for Machine Learning Model Training and ValidationScientific Data10.1038/s41597-024-04328-311:1Online publication date: 28-Dec-2024
https://doi.org/10.1038/s41597-024-04328-3
Mahajan PKaur P(2024) Improving cervical cancer classification in PAP smear images with enhanced segmentation and deep progressive learning‐based techniques Diagnostic Cytopathology10.1002/dc.2529552:6(313-324)Online publication date: 22-Mar-2024
https://doi.org/10.1002/dc.25295
Jiang ZMessner J(2023)Computer Vision Applications In Construction And Asset Management Phases: A Literature ReviewJournal of Information Technology in Construction10.36680/j.itcon.2023.00928(176-199)Online publication date: 3-Apr-2023
https://doi.org/10.36680/j.itcon.2023.009
Fekri-Ershad SAlsaffar M(2023)Developing a Tuned Three-Layer Perceptron Fed with Trained Deep Convolutional Neural Networks for Cervical Cancer DiagnosisDiagnostics10.3390/diagnostics1304068613:4(686)Online publication date: 12-Feb-2023
https://doi.org/10.3390/diagnostics13040686
Dash SSethy PBehera S(2023)Cervical Transformation Zone Segmentation and Classification based on Improved Inception-ResNet-V2 Using Colposcopy ImagesCancer Informatics10.1177/1176935123116147722Online publication date: 29-Mar-2023
https://doi.org/10.1177/11769351231161477
Rastogi PKhanna KSingh V(2023) Classification of single‐cell cervical pap smear images using EfficientNet Expert Systems10.1111/exsy.1341840:10Online publication date: 8-Aug-2023
https://doi.org/10.1111/exsy.13418
Badirli SPicard CMohler GRichert FAkata ZDundar M(2023)Classifying the unknown: Insect identification with deep hierarchical Bayesian learningMethods in Ecology and Evolution10.1111/2041-210X.1410414:6(1515-1530)Online publication date: 19-Apr-2023
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