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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) · hlm. 338–349

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

Abstrak

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.

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  • Jenderal Soedirman University
  • Nara Institute of Science and Technology
  • National Research and Innovation Agency
  • University of Technology Malaysia
  • State University of Jakarta
  • Mercu Buana University
  • Riau University
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Rekan penulisOrganisasiArtikel bersama
Haris Budi WidodoJenderal Soedirman University9
Dina RahmawatiJenderal Soedirman University6
Madya Ardi WicaksonoJenderal Soedirman University6
Syafrudin Rais AkhdanNara Institute of Science and Technology6
Mukhtar EffendiJenderal Soedirman University4
Sib Krishna GhoshalUniversity of Technology Malaysia4
Candra KurniawanNational Research and Innovation Agency4
Ariep Soelaiman SetiadiJenderal Soedirman University4
Wahyu SiswandariJenderal Soedirman University4
Erfan HandokoState University of Jakarta3
Arif Pujo RiyantoJenderal Soedirman University3
Muhammad Syaiful AliimJenderal Soedirman University3
Muhammad Irham AkbarJenderal Soedirman University3
Rafi Satria Dwi AwandaJenderal Soedirman University3
Putra Dwi SusantoJenderal Soedirman University3
Alfin ChrisantyJenderal Soedirman University2
Pangestu F. WibowoJenderal Soedirman University2
Fibra R. FirmandaJenderal Soedirman University2
Muhammad Rifqi KurniawanJenderal Soedirman University2
Andreas Sahir AryantoJenderal Soedirman University2
Mohammad Irham AkbarJenderal Soedirman University2
Eman SutrisnaJenderal Soedirman University2
Lazuardi Umar ARiau University2
Tomy TamtowiJenderal Soedirman University2
Jumaeda JatmikaNational Research and Innovation Agency2
Siti Uswatun HasanahJenderal Soedirman University1
Sunardi SunardiJenderal Soedirman University1
Suroso SurosoJenderal Soedirman University1
Winasis WinasisJenderal Soedirman University1
Sinta Nur Mazida RafiqJenderal Soedirman University1
Mudrik AlaydrusMercu Buana University1
Wahyu Tri CahyantoJenderal Soedirman University1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (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>