RepoMedUNM: A New Dataset for Feature Extraction and Training of Deep Learning Network for Classification of Pap Smear Images

Dwiza Riana; Sri Hadianti; Sri Rahayu; Frieyadie; Muhamad Hasan; Izni Nur Karimah; Rafly Pratama

doi:10.1007/978-3-030-92307-5_37

RepoMedUNM: A New Dataset for Feature Extraction and Training of Deep Learning Network for Classification of Pap Smear Images

Dwiza Riana^*
, Sri Hadianti
, Sri Rahayu
, Frieyadie
, Muhamad Hasan
, Izni Nur Karimah
, Rafly Pratama

^*Corresponding author for this work

Universitas Nusa Mandiri

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Citations (Scopus)

Abstract

Morphological changes in the cell structure in Pap Smear images are the basis for classification in pathology. Identification of this classification is a challenge because of the complexity of Pap Smear images caused by changes in cell morphology. This procedure is very important because it provides basic information for detecting cancerous or precancerous lesions. To help advance research in this area, we present the RepoMedUNM Pap smear image database consisting of non-ThinPrep (nTP) Pap test images and ThinPrep (TP) Pap test images. It is common for research groups to have their image datasets. This need is driven by the fact that established datasets are not publicly accessible. The purpose of this study is to present the RepoMedUNM dataset analysis performed for texture feature cells on new images consisting of four classes, normal, L-Sil, H-Sil, and Koilocyt with K-means segmentation. Evaluation of model classification using reuse pretrained network method. Convolutional Neural Network (CNN) implements the pre-trained CNN VGG16, VGG19, and ResNet50 models for the classification of three groups namely TP, nTP and all datasets. The results of feature cells and classification can be used as a reference for the evaluation of future classification techniques.

Original language	English
Title of host publication	Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings
Editors	Teddy Mantoro, Minho Lee, Media Anugerah Ayu, Kok Wai Wong, Achmad Nizar Hidayanto
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	317-325
Number of pages	9
ISBN (Print)	9783030923068
DOIs	https://doi.org/10.1007/978-3-030-92307-5_37
Publication status	Published - 2021
Externally published	Yes
Event	28th International Conference on Neural Information Processing, ICONIP 2021 - Virtual, Online Duration: 8 Dec 2021 → 12 Dec 2021

Publication series

Name	Communications in Computer and Information Science
Volume	1516 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	28th International Conference on Neural Information Processing, ICONIP 2021
City	Virtual, Online
Period	8/12/21 → 12/12/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Cell image database
Cervical cell classification
Convolutional Neural Network
K-Means
Pap smear images
ThinPrep

Access to Document

10.1007/978-3-030-92307-5_37

Cite this

Riana, D., Hadianti, S., Rahayu, S., Frieyadie, Hasan, M., Karimah, I. N., & Pratama, R. (2021). RepoMedUNM: A New Dataset for Feature Extraction and Training of Deep Learning Network for Classification of Pap Smear Images. In T. Mantoro, M. Lee, M. A. Ayu, K. W. Wong, & A. N. Hidayanto (Eds.), Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings (pp. 317-325). (Communications in Computer and Information Science; Vol. 1516 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-92307-5_37

Riana, Dwiza ; Hadianti, Sri ; Rahayu, Sri et al. / RepoMedUNM : A New Dataset for Feature Extraction and Training of Deep Learning Network for Classification of Pap Smear Images. Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings. editor / Teddy Mantoro ; Minho Lee ; Media Anugerah Ayu ; Kok Wai Wong ; Achmad Nizar Hidayanto. Springer Science and Business Media Deutschland GmbH, 2021. pp. 317-325 (Communications in Computer and Information Science).

@inproceedings{bd5876775e4c45b3ac7694168481c435,

title = "RepoMedUNM: A New Dataset for Feature Extraction and Training of Deep Learning Network for Classification of Pap Smear Images",

abstract = "Morphological changes in the cell structure in Pap Smear images are the basis for classification in pathology. Identification of this classification is a challenge because of the complexity of Pap Smear images caused by changes in cell morphology. This procedure is very important because it provides basic information for detecting cancerous or precancerous lesions. To help advance research in this area, we present the RepoMedUNM Pap smear image database consisting of non-ThinPrep (nTP) Pap test images and ThinPrep (TP) Pap test images. It is common for research groups to have their image datasets. This need is driven by the fact that established datasets are not publicly accessible. The purpose of this study is to present the RepoMedUNM dataset analysis performed for texture feature cells on new images consisting of four classes, normal, L-Sil, H-Sil, and Koilocyt with K-means segmentation. Evaluation of model classification using reuse pretrained network method. Convolutional Neural Network (CNN) implements the pre-trained CNN VGG16, VGG19, and ResNet50 models for the classification of three groups namely TP, nTP and all datasets. The results of feature cells and classification can be used as a reference for the evaluation of future classification techniques.",

keywords = "Cell image database, Cervical cell classification, Convolutional Neural Network, K-Means, Pap smear images, ThinPrep",

author = "Dwiza Riana and Sri Hadianti and Sri Rahayu and Frieyadie and Muhamad Hasan and Karimah, \{Izni Nur\} and Rafly Pratama",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 28th International Conference on Neural Information Processing, ICONIP 2021 ; Conference date: 08-12-2021 Through 12-12-2021",

year = "2021",

doi = "10.1007/978-3-030-92307-5\_37",

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editor = "Teddy Mantoro and Minho Lee and Ayu, \{Media Anugerah\} and Wong, \{Kok Wai\} and Hidayanto, \{Achmad Nizar\}",

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}

Riana, D, Hadianti, S, Rahayu, S, Frieyadie, Hasan, M, Karimah, IN & Pratama, R 2021, RepoMedUNM: A New Dataset for Feature Extraction and Training of Deep Learning Network for Classification of Pap Smear Images. in T Mantoro, M Lee, MA Ayu, KW Wong & AN Hidayanto (eds), Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings. Communications in Computer and Information Science, vol. 1516 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 317-325, 28th International Conference on Neural Information Processing, ICONIP 2021, Virtual, Online, 8/12/21. https://doi.org/10.1007/978-3-030-92307-5_37

RepoMedUNM: A New Dataset for Feature Extraction and Training of Deep Learning Network for Classification of Pap Smear Images. / Riana, Dwiza; Hadianti, Sri; Rahayu, Sri et al.
Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings. ed. / Teddy Mantoro; Minho Lee; Media Anugerah Ayu; Kok Wai Wong; Achmad Nizar Hidayanto. Springer Science and Business Media Deutschland GmbH, 2021. p. 317-325 (Communications in Computer and Information Science; Vol. 1516 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Riana, Dwiza

AU - Hadianti, Sri

AU - Rahayu, Sri

AU - Frieyadie,

AU - Hasan, Muhamad

AU - Karimah, Izni Nur

AU - Pratama, Rafly

PY - 2021

Y1 - 2021

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AB - Morphological changes in the cell structure in Pap Smear images are the basis for classification in pathology. Identification of this classification is a challenge because of the complexity of Pap Smear images caused by changes in cell morphology. This procedure is very important because it provides basic information for detecting cancerous or precancerous lesions. To help advance research in this area, we present the RepoMedUNM Pap smear image database consisting of non-ThinPrep (nTP) Pap test images and ThinPrep (TP) Pap test images. It is common for research groups to have their image datasets. This need is driven by the fact that established datasets are not publicly accessible. The purpose of this study is to present the RepoMedUNM dataset analysis performed for texture feature cells on new images consisting of four classes, normal, L-Sil, H-Sil, and Koilocyt with K-means segmentation. Evaluation of model classification using reuse pretrained network method. Convolutional Neural Network (CNN) implements the pre-trained CNN VGG16, VGG19, and ResNet50 models for the classification of three groups namely TP, nTP and all datasets. The results of feature cells and classification can be used as a reference for the evaluation of future classification techniques.

KW - Cell image database

KW - Cervical cell classification

KW - Convolutional Neural Network

KW - K-Means

KW - Pap smear images

KW - ThinPrep

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Riana D, Hadianti S, Rahayu S, Frieyadie, Hasan M, Karimah IN et al. RepoMedUNM: A New Dataset for Feature Extraction and Training of Deep Learning Network for Classification of Pap Smear Images. In Mantoro T, Lee M, Ayu MA, Wong KW, Hidayanto AN, editors, Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 317-325. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-92307-5_37