research-article

Automatic Nucleus Detection of Pap Smear Images using Stacked Sparse Autoencoder (SSAE)

Authors:

Moh. Faturrahman,

Fakhirah D. GhaisaniAuthors Info & Claims

ICACS '17: Proceedings of the 1st International Conference on Algorithms, Computing and Systems

Pages 9 - 13

https://doi.org/10.1145/3127942.3127946

Published: 10 August 2017 Publication History

ICACS '17: Proceedings of the 1st International Conference on Algorithms, Computing and Systems

Automatic Nucleus Detection of Pap Smear Images using Stacked Sparse Autoencoder (SSAE)

Pages 9 - 13

Abstract
References

Abstract

Pap smear image analysis is an effective and common way for early diagnosis of cervical cancer. Nucleus and cytoplasm morphology analysis are main criterion in determining whether the cells are normal or abnormal. Therefore, the accuracy of nucleus detection is crucial before further analysis of cell changes. One of the main problem in automatic nucleus detection process on pap smear image is how to accurately detect the nucleus on multi-cell image which usually contain overlapped cells. To solve the problem, authors propose a deep learning (DL) approach in particular Stacked Sparse Autoencoder (SSAE) as a feature representation process in multi-cell pap smear images. SSAE is able to capture high level feature through learning processing from low level feature (pixel). The high level feature will be a differentiator feature between nucleus and non-nucleus. In this research, authors have applied sliding window operation (SWO) on pap smear images and utilized softmax classifier (SMC) for the nucleus classification process. The main purpose in this research is to measure the performance of SSAE+SMC for the detection of nucleus on overlapped cells. The result shows that fine-tuned SSAE+SMC has significantly increased the accuracy of nucleus detection. The best accuracy achieves 0.876 on 50 x 50 window size.

References

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Cited By

Alahmari SGoldgof DHall LMouton P(2024)A Review of Nuclei Detection and Segmentation on Microscopy Images Using Deep Learning With Applications to Unbiased Stereology CountingIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.321340735:6(7458-7477)Online publication date: Jun-2024
https://doi.org/10.1109/TNNLS.2022.3213407
Li JZhang XHuang LLi FDuan SSun Y(2022)Speech Emotion Recognition Using a Dual-Channel Complementary Spectrogram and the CNN-SSAE Neutral NetworkApplied Sciences10.3390/app1219951812:19(9518)Online publication date: 22-Sep-2022
https://doi.org/10.3390/app12199518
Nahrawi NMustafa WKanafiah SAhmad WRohani MRahim H(2021)A Novel Nucleus Detection on Pap Smear Image Using Mathematical Morphology ApproachJournal of Biomimetics, Biomaterials and Biomedical Engineering10.4028/www.scientific.net/JBBBE.49.5349(53-61)Online publication date: Feb-2021
https://doi.org/10.4028/www.scientific.net/JBBBE.49.53
Show More Cited By

Index Terms

Automatic Nucleus Detection of Pap Smear Images using Stacked Sparse Autoencoder (SSAE)
1. Applied computing
  1. Life and medical sciences

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Highlights
- An automated nucleus detection framework based on a stacked sparse autoencoder (SSAE) and a case-based postprocessing method (CPM) is proposed.
Graphical abstract

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Abstract
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cover image ACM Other conferences

ICACS '17: Proceedings of the 1st International Conference on Algorithms, Computing and Systems

August 2017

117 pages

ISBN:9781450352840

DOI:10.1145/3127942

Copyright © 2017 ACM.

© 2017 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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New York, NY, United States

Publication History

Published: 10 August 2017

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Universitas Indonesia

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ICACS '17

ICACS '17: 2017 International Conference on Algorithms, Computing and Systems

August 10 - 13, 2017

Jeju Island, Republic of Korea

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Cited By

Alahmari SGoldgof DHall LMouton P(2024)A Review of Nuclei Detection and Segmentation on Microscopy Images Using Deep Learning With Applications to Unbiased Stereology CountingIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.321340735:6(7458-7477)Online publication date: Jun-2024
https://doi.org/10.1109/TNNLS.2022.3213407
Li JZhang XHuang LLi FDuan SSun Y(2022)Speech Emotion Recognition Using a Dual-Channel Complementary Spectrogram and the CNN-SSAE Neutral NetworkApplied Sciences10.3390/app1219951812:19(9518)Online publication date: 22-Sep-2022
https://doi.org/10.3390/app12199518
Nahrawi NMustafa WKanafiah SAhmad WRohani MRahim H(2021)A Novel Nucleus Detection on Pap Smear Image Using Mathematical Morphology ApproachJournal of Biomimetics, Biomaterials and Biomedical Engineering10.4028/www.scientific.net/JBBBE.49.5349(53-61)Online publication date: Feb-2021
https://doi.org/10.4028/www.scientific.net/JBBBE.49.53
Kavyashree CSowmya HReddy N(2021)A Survey on the Cervical Cancer Detection using Deep Learning methods2021 International Conference on Forensics, Analytics, Big Data, Security (FABS)10.1109/FABS52071.2021.9702701(1-6)Online publication date: 21-Dec-2021
https://doi.org/10.1109/FABS52071.2021.9702701
Buettner RBilo MBay NZubac T(2020)A Systematic Literature Review of Medical Image Analysis Using Deep Learning2020 IEEE Symposium on Industrial Electronics & Applications (ISIEA)10.1109/ISIEA49364.2020.9188131(1-4)Online publication date: Jul-2020
https://doi.org/10.1109/ISIEA49364.2020.9188131
Baykal EDogan HErcin MErsoz SEkinci M(2020)Modern convolutional object detectors for nuclei detection on pleural effusion cytology imagesMultimedia Tools and Applications10.1007/s11042-019-7461-379:21-22(15417-15436)Online publication date: 1-Jun-2020
https://dl.acm.org/doi/10.1007/s11042-019-7461-3

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