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Deep learning-based cervical cancer detection via colposcopy images integrated into an Android mobile application

IAES International Journal of Artificial Intelligence · Vol. 15 No. 1 (2026) · pp. 338–349

DOI
10.11591/ijai.v15.i1.pp338-349
ISSN
2252-8938, 2089-4872
Published
2026-02-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Cervical cancer is a form of cancer that develops in the cells of the cervix, the lower part of the uterus that connects the uterus to the vagina. Early detection is essential for improving the chances of recovery from cervical cancer. One method for early detection is colposcopy image analysis, a medical procedure that examines the cervix and captures images for evaluation. These images were analyzed to observe color changes after the visual inspection with acetic acid (VIA) process. However, this analysis requires experienced and specially trained medical personnel. To address this challenge, a system that can automatically classify cervical cancer images is needed. Therefore, researchers proposed designing and developing an Android mobile application to enable early detection of cervical cancer using the convolutional neural network (CNN) algorithm. The CNN model was tested using test data to evaluate its performance. The optimized CNN model utilizing the ResNet50 architecture achieved 86% test accuracy, 85% precision, and 87% recall. The test results indicate that the model's accuracy is consistent before and after its implementation on the mobile application, confirming the effectiveness of both the model and its implementation as diagnostic tools.

Keywords

Collaboration network of the authors

40 authors · 140 co-authorship links · 11 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Jenderal Soedirman University
  • Nara Institute of Science and Technology
  • Shizuoka University
  • State University of Semarang
  • Institut Agama Islam Negeri Ternate
  • Mercu Buana University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Haris Budi WidodoJenderal Soedirman University9
Winasis WinasisAffiliation unknown7
Dina RahmawatiInstitut Agama Islam Negeri Ternate6
Syafrudin Rais AkhdanAffiliation unknown6
Toshihiko NoguchiShizuoka University4
Mukhtar EffendiAffiliation unknown4
Sib Krishna GhoshalAffiliation unknown4
Candra KurniawanAffiliation unknown4
Ariep Soelaiman SetiadiJenderal Soedirman University4
Wahyu SiswandariJenderal Soedirman University4
Dewi Liesnoor SetyowatiState University of Semarang3
Erni SuhariniAffiliation unknown3
Hari SiswantoroAffiliation unknown3
Priswanto PriswantoAffiliation unknown3
Mudrik AlaydrusMercu Buana University3
Erfan HandokoAffiliation unknown3
Arif Pujo RiyantoJenderal Soedirman University3
Muhammad Syaiful AliimJenderal Soedirman University3
Muhammad Irham AkbarJenderal Soedirman University3
Sriyanto SriyantoAffiliation unknown2
Ananto AjiAffiliation unknown2
Hariyanto HariyantoAffiliation unknown2
Heri TjahjonoAffiliation unknown2
Dwi Ayu LestariAffiliation unknown2
Wahyu SetyaningsihAffiliation unknown2
Daru Tri NugrohoJenderal Soedirman University2
Alfin ChrisantyJenderal Soedirman University2
Pangestu F. WibowoJenderal Soedirman University2
Fibra R. FirmandaJenderal Soedirman University2
Muhammad Rifqi KurniawanJenderal Soedirman University2
Apik Budi SantosoAffiliation unknown1
agus wahit rohmanAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Deep learning-based cervical cancer detection via colposcopy  images integrated into an Android mobile application</dc:title>
	<dc:creator>Supriyanti, Retno</dc:creator>
	<dc:creator>Anzil, Arsil Kultura</dc:creator>
	<dc:creator>Ramadhani, Yogi</dc:creator>
	<dc:creator>Suroso, Suroso</dc:creator>
	<dc:creator>Widanarto, Wahyu</dc:creator>
	<dc:creator>Alqaaf, Muhammad</dc:creator>
	<dc:creator>Dwi Hapsari, Kartika</dc:creator>
	<dc:creator>Diana Kartika, Futiat</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Cervical cancer;  Convolutional neural networks;  Early detection;  Mobile application; Visual inspection</dc:subject>
	<dc:description xml:lang="en-US">Cervical cancer is a form of cancer that develops in the cells of the cervix, the lower part of the uterus that connects the uterus to the vagina. Early detection is essential for improving the chances of recovery from cervical cancer. One method for early detection is colposcopy image analysis, a medical procedure that examines the cervix and captures images for evaluation. These images were analyzed to observe color changes after the visual inspection with acetic acid (VIA) process. However, this analysis requires experienced and specially trained medical personnel. To address this challenge, a system that can automatically classify cervical cancer images is needed. Therefore, researchers proposed designing and developing an Android mobile application to enable early detection of cervical cancer using the convolutional neural network (CNN) algorithm. The CNN model was tested using test data to evaluate its performance. The optimized CNN model utilizing the ResNet50 architecture achieved 86% test accuracy, 85% precision, and 87% recall. The test results indicate that the model's accuracy is consistent before and after its implementation on the mobile application, confirming the effectiveness of both the model and its implementation as diagnostic tools.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">The Ministry of Higher Education, Science, and Technology of the Republic of Indonesia</dc:contributor>
	<dc:date>2026-02-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/27509</dc:identifier>
	<dc:identifier>10.11591/ijai.v15.i1.pp338-349</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 15, No 1: February 2026; 338-349</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v15.i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/27509/14942</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Retno Supriyanti, Arsil Kultura Anzil, Yogi Ramadhani, Suroso, Wahyu Widanarto,  Muhammad Alqaaf, Kartika Dwi Hapsari, Futiat Diana Kartika</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>