Araştırma Makalesi
BibTex RIS Kaynak Göster

Yıl 2020, , 99 - 111, 28.08.2020

Fatma Betül Akyol Oğuz Altun

https://doi.org/10.35377/saucis.03.02.722670
Cited By: 4

Öz

Teşekkür

Bu çalışmada rahim ağzı kanseri ve hastalığın teşhisi hakkında bilgi veren İstanbul Zeynep Kâmil Kadın ve Çocuk Hastalıkları Eğitim ve Araştırma Hastanesi Patoloji Bölümü’ne teşekkür ederim.

Kaynakça

  • "National Institutes of Health homepage", 2020. [Online]. Available: https://www.cancer.gov/about-cancer/understanding/what-is-cancer). [Accessed: 01-Feb-2020].
  • Cancer Today, "Global Cancer Observatory homepage", 2018. [Online]. Available: http://gco.iarc.fr/today/online-analysis-table. [Accessed: 02-Feb-2020].
  • DB. Cooper, C.E. McCathran, "Cervical Dysplasia", StatPearls [Internet]. StatPearls Publishing, 2019.
  • RJ. Kurman, D. Solomon, The Bethesda System for Reporting Cervical/Vaginal Cytologic Diagnoses. New York: Springer-Verlag, 1994.
  • S.E. Waggoner, "Cervical cancer", The Lancet, vol. 361, no. 9376, pp. 2217-2225, 2003.
  • T. Bilal, J. Dias, and N. Werghi, "Classification of cervical-cancer using pap-smear images: a convolutional neural network approach", Annual Conference on Medical Image Understanding and Analysis, Springer, Cham, 2017.
  • M.E. Plissiti, N. Christophoros, and A. Charchanti, "Automated detection of cell nuclei in pap smear images using morphological reconstruction and clustering", IEEE Transactions on information technology in biomedicine, vol. 15, no.2, pp. 233-241, 2010.
  • P. Wang, L. Wang, Y. Li, Q. Song, S. Lv, X. Hu, “Automatic cell nuclei segmentation and classification of cervical Pap smear images”, Biomedical Signal Processing and Control, vol. 48, pp. 93-103, 2019.
  • Y. Marinakis, G. Dounias, and J. Jantzen, "Pap smear diagnosis using a hybrid intelligent scheme focusing on genetic algorithm based feature selection and nearest neighbor classification", Computers in Biology and Medicine, vol. 39, no.1, pp. 69-78, 2009.
  • A. GençTav, S. Aksoy, and S. Önder, "Unsupervised segmentation and classification of cervical cell images", Pattern recognition, vol. 45, no.12, pp. 4151-4168, 2012.
  • H.A. Phoulady, "A framework for nucleus and overlapping cytoplasm segmentation in cervical cytology extended depth of field and volume images", Computerized Medical Imaging and Graphics, vol. 59, pp. 38-49, 2017.
  • K.P. Win, Y. Kitjaidure, K. Hamamoto, T. Myo Aung,"Computer-Assisted Screening for Cervical Cancer Using Digital Image Processing of Pap Smear Images", Appl. Sci., vol. 10, no.5, pp.1800, 2020.
  • W. William, A. Ware, A.H. Basaza-Ejiri, & J. Obungoloch, "A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images", Biomedical engineering online, vol.18, no.1, pp.16, 2019.
  • V. Vapnik, The nature of statistical learning theory. New York: Springer-Verlag, 1995.
  • M. Pal, "Random forest classifier for remote sensing classification", International Journal of Remote Sensing, vol.26, no.1, pp. 217-222, 2005.
  • M.W. Gardner, S.R. Dorling, "Artificial neural networks (the multilayer perceptron) —a review of applications in the atmospheric sciences", Atmospheric Environment, vol. 32, no. 14–15, pp. 2627-2636,1998.
  • Y. Liao, V. R. Vemuri, "Use of k-nearest neighbor classifier for intrusion detection", Computers & security, vol. 21, no.5, pp. 439-448, 2002.
  • M. M. Saritas, A. Yasar, "Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification", International Journal of Intelligent Systems and Applications in Engineering, vol. 7, no. 2, pp. 88-91, 2019.
  • S.K. Shevade, S. S. Keerthi, "A simple and efficient algorithm for gene selection using sparse logistic regression", Bioinformatics, vol. 19, no. 17, pp. 2246-2253, 2003.
  • MDE-lab, "MDE-lab downloadspage,"2011.[Online]. Available: http://mde-lab.aegean.gr/downloads. [Accessed: 30-Sep-2019].
  • J. Jantzen, J. Norup, Jonas, G. Dounias, B. Bjerregaard, "Pap-smear Benchmark Data For Pattern Classification", Nature Inspired Smart Information Systems (NiSIS), pp. 1-9, 2005.
  • N. Nill, “A visual model weighted cosine transform for image compression and quality assessment”, IEEE Transactions on communications, vol.33, no. 6, pp. 551-557, 1985.
  • M. E. Plissiti, P. Dimitrakopoulos, G. Sfikas, C. Nikou, O. Krikoni, A. Charchanti, SIPAKMED: A new dataset for feature and image based classification of normal and pathological cervical cells in Pap smear images, IEEE International Conference on Image Processing (ICIP) 2018, Athens, Greece, 7-10 October 2018.
  • H. Demirel, G. Anbarjafari, "Discrete Wavelet Transform-Based Satellite Image Resolution Enhancement", IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 6, pp. 1997-2004, 2011.
  • H. Demirel, G. Anbarjafari, “Image resolution enhancement by using discrete and stationary wavelet decomposition”, IEEE transactions on image processing, vol. 20, no. 5, pp. 1458-1460, 2010.
  • E.S. Cibas, B.S. Ducatman, Cytology E-Book: Diagnostic principles and clinical correlates, Elsevier Health Sciences, 2013.
  • "Eurocytology Cervical Cytology homepage", 2020. [Online]. Available: https://www.eurocytology.eu/en/course/1292. [Accessed: 15-Mar-2020].
  • T. Chankong, N. Theera-Umpon, & S. Auephanwiriyakul, "Automatic cervical cell segmentation and classification in Pap smears", Computer methods and programs in biomedicine, vol.113, no.2, pp.539-556, 2014.
  • W. William, A. Ware, A.H. Basaza-Ejiri, & J. Obungoloch, "Cervical cancer classification from Pap-smears using an enhanced fuzzy C-means algorithm", Informatics in Medicine Unlocked, vol.14, pp. 23-33, 2019.
  • L. Zhang, L. Lu, I. Nogues, R.M. Summers, S. Liu, & J. Yao, “DeepPap: deep convolutional networks for cervical cell classification”, IEEE journal of biomedical and health informatics, vol. 21, no.6, pp. 1633-1643, 2017.

Detection of Cervix Cancer from Pap-smear Images

Yıl 2020, , 99 - 111, 28.08.2020

Fatma Betül Akyol Oğuz Altun

https://doi.org/10.35377/saucis.03.02.722670
Cited By: 4

Öz

Pap-smear test is used to detect cervical cancer, which ranks fourth in the ranking of cancer diseases in women worldwide. In this study, it is aimed to design a computer based decision system that can detect cervical cancer at an early stage. Normal and abnormal cells are found in the cervix images obtained as a result of the pap-smear test and the abnormal cells are marked on the image. The features extracted from the images were examined with pathologists and a dataset was created. For each of the 917 images in the Herlev dataset, these features were extracted and stored in a dataset. Support Vector Machines (SVM), Naive Bayes, Random Forest (RF), Multilayer Perceptron (MLP), Logistic Regression (LR), K- Nearest Neighbor (KNN) methods were applied to the created dataset, and accuracy values between 83% and 92% were obtained.

Anahtar Kelimeler

Cervical cancer Pap-smear Segmentation Classification

Kaynakça

  • "National Institutes of Health homepage", 2020. [Online]. Available: https://www.cancer.gov/about-cancer/understanding/what-is-cancer). [Accessed: 01-Feb-2020].
  • Cancer Today, "Global Cancer Observatory homepage", 2018. [Online]. Available: http://gco.iarc.fr/today/online-analysis-table. [Accessed: 02-Feb-2020].
  • DB. Cooper, C.E. McCathran, "Cervical Dysplasia", StatPearls [Internet]. StatPearls Publishing, 2019.
  • RJ. Kurman, D. Solomon, The Bethesda System for Reporting Cervical/Vaginal Cytologic Diagnoses. New York: Springer-Verlag, 1994.
  • S.E. Waggoner, "Cervical cancer", The Lancet, vol. 361, no. 9376, pp. 2217-2225, 2003.
  • T. Bilal, J. Dias, and N. Werghi, "Classification of cervical-cancer using pap-smear images: a convolutional neural network approach", Annual Conference on Medical Image Understanding and Analysis, Springer, Cham, 2017.
  • M.E. Plissiti, N. Christophoros, and A. Charchanti, "Automated detection of cell nuclei in pap smear images using morphological reconstruction and clustering", IEEE Transactions on information technology in biomedicine, vol. 15, no.2, pp. 233-241, 2010.
  • P. Wang, L. Wang, Y. Li, Q. Song, S. Lv, X. Hu, “Automatic cell nuclei segmentation and classification of cervical Pap smear images”, Biomedical Signal Processing and Control, vol. 48, pp. 93-103, 2019.
  • Y. Marinakis, G. Dounias, and J. Jantzen, "Pap smear diagnosis using a hybrid intelligent scheme focusing on genetic algorithm based feature selection and nearest neighbor classification", Computers in Biology and Medicine, vol. 39, no.1, pp. 69-78, 2009.
  • A. GençTav, S. Aksoy, and S. Önder, "Unsupervised segmentation and classification of cervical cell images", Pattern recognition, vol. 45, no.12, pp. 4151-4168, 2012.
  • H.A. Phoulady, "A framework for nucleus and overlapping cytoplasm segmentation in cervical cytology extended depth of field and volume images", Computerized Medical Imaging and Graphics, vol. 59, pp. 38-49, 2017.
  • K.P. Win, Y. Kitjaidure, K. Hamamoto, T. Myo Aung,"Computer-Assisted Screening for Cervical Cancer Using Digital Image Processing of Pap Smear Images", Appl. Sci., vol. 10, no.5, pp.1800, 2020.
  • W. William, A. Ware, A.H. Basaza-Ejiri, & J. Obungoloch, "A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images", Biomedical engineering online, vol.18, no.1, pp.16, 2019.
  • V. Vapnik, The nature of statistical learning theory. New York: Springer-Verlag, 1995.
  • M. Pal, "Random forest classifier for remote sensing classification", International Journal of Remote Sensing, vol.26, no.1, pp. 217-222, 2005.
  • M.W. Gardner, S.R. Dorling, "Artificial neural networks (the multilayer perceptron) —a review of applications in the atmospheric sciences", Atmospheric Environment, vol. 32, no. 14–15, pp. 2627-2636,1998.
  • Y. Liao, V. R. Vemuri, "Use of k-nearest neighbor classifier for intrusion detection", Computers & security, vol. 21, no.5, pp. 439-448, 2002.
  • M. M. Saritas, A. Yasar, "Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification", International Journal of Intelligent Systems and Applications in Engineering, vol. 7, no. 2, pp. 88-91, 2019.
  • S.K. Shevade, S. S. Keerthi, "A simple and efficient algorithm for gene selection using sparse logistic regression", Bioinformatics, vol. 19, no. 17, pp. 2246-2253, 2003.
  • MDE-lab, "MDE-lab downloadspage,"2011.[Online]. Available: http://mde-lab.aegean.gr/downloads. [Accessed: 30-Sep-2019].
  • J. Jantzen, J. Norup, Jonas, G. Dounias, B. Bjerregaard, "Pap-smear Benchmark Data For Pattern Classification", Nature Inspired Smart Information Systems (NiSIS), pp. 1-9, 2005.
  • N. Nill, “A visual model weighted cosine transform for image compression and quality assessment”, IEEE Transactions on communications, vol.33, no. 6, pp. 551-557, 1985.
  • M. E. Plissiti, P. Dimitrakopoulos, G. Sfikas, C. Nikou, O. Krikoni, A. Charchanti, SIPAKMED: A new dataset for feature and image based classification of normal and pathological cervical cells in Pap smear images, IEEE International Conference on Image Processing (ICIP) 2018, Athens, Greece, 7-10 October 2018.
  • H. Demirel, G. Anbarjafari, "Discrete Wavelet Transform-Based Satellite Image Resolution Enhancement", IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 6, pp. 1997-2004, 2011.
  • H. Demirel, G. Anbarjafari, “Image resolution enhancement by using discrete and stationary wavelet decomposition”, IEEE transactions on image processing, vol. 20, no. 5, pp. 1458-1460, 2010.
  • E.S. Cibas, B.S. Ducatman, Cytology E-Book: Diagnostic principles and clinical correlates, Elsevier Health Sciences, 2013.
  • "Eurocytology Cervical Cytology homepage", 2020. [Online]. Available: https://www.eurocytology.eu/en/course/1292. [Accessed: 15-Mar-2020].
  • T. Chankong, N. Theera-Umpon, & S. Auephanwiriyakul, "Automatic cervical cell segmentation and classification in Pap smears", Computer methods and programs in biomedicine, vol.113, no.2, pp.539-556, 2014.
  • W. William, A. Ware, A.H. Basaza-Ejiri, & J. Obungoloch, "Cervical cancer classification from Pap-smears using an enhanced fuzzy C-means algorithm", Informatics in Medicine Unlocked, vol.14, pp. 23-33, 2019.
  • L. Zhang, L. Lu, I. Nogues, R.M. Summers, S. Liu, & J. Yao, “DeepPap: deep convolutional networks for cervical cell classification”, IEEE journal of biomedical and health informatics, vol. 21, no.6, pp. 1633-1643, 2017.
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka
Bölüm Makaleler
Yazarlar

Fatma Betül Akyol 0000-0002-3836-1317

Oğuz Altun 0000-0001-8823-3148

Yayımlanma Tarihi 28 Ağustos 2020
Gönderilme Tarihi 18 Nisan 2020
Kabul Tarihi 21 Mayıs 2020
Yayımlandığı Sayı Yıl 2020

Kaynak Göster

IEEE F. B. Akyol ve O. Altun, “Detection of Cervix Cancer from Pap-smear Images”, SAUCIS, c. 3, sy. 2, ss. 99–111, 2020, doi: 10.35377/saucis.03.02.722670.

Cited By

Deep learning-based approaches for robust classification of cervical cancer
Neural Computing and Applications
https://doi.org/10.1007/s00521-023-08757-w
Cervical Cancer Prediction Using SMOTE Algorithm and Machine Learning Approaches
Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi
https://doi.org/10.21597/jist.1222764
A Deep Neural Network for Cervical Cell Classification Based on Cytology Images
IEEE Access
https://doi.org/10.1109/ACCESS.2022.3230280
Derin öğrenme temelli nesne tespiti algoritmaları kullanılarak kişiye özgü reklam sunulması
Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi
https://doi.org/10.25092/baunfbed.878224

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