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Open access Jurnal SINTA 2

Decision Support System for Gastrointestinal Cancer Detection Using Deep Learning

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 20 No. 3 (2026) · pp. 271–282

DOI
10.22146/ijccs.115717
ISSN
2460-7258, 1978-1520
Published
2026-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Gastrointestinal diseases are health problems that require the help of medical image analysis to improve accuracy and consistency in clinical decision-making. The main challenges with multilabel classification are visual complexity and morphological similarity. The aim of this study was to develop and evaluate an in-depth learning approach to build the first gastrointestinal tract image-based Clinical Decision Support System (CDSS). This dataset is publicly available and consists of 14 classes of gastrointestinal conditions with a total of 8,750 images, including 7,000 training images and 1,750 test images with a balanced distribution of classes. Four pre-trained convolutional neural network architectures were compared, namely MobileNetV2, MobileNetV3-Small, EfficientNet-B0, and ResNet50. The evaluation metrics used were accuracy, precision, recall, F1-score, confusion matrix, and case study inference. The experimental results showed that ResNet50 outperformed the others with 88.97% accuracy, 89.13% accuracy, 88.97% recall, and 88.94% F1-score, with multiple class analyses. Single-case inference testing on six randomly selected test images obtained a confidence value between 90-99%. The selected model is integrated into the mobile CDSS app to provide a level of confidence along with the predicted outcome. This method will likely allow for fundamental image-based evaluation to be more consistent and accountable in supporting clinical decision-making.

Keywords

Collaboration network of the authors

2 authors · 1 co-authorship link · 1 across different organisations

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  • Institut Teknologi Telkom Purwokerto
  • Telkom University
  • Different organisations
  • Same organisation
  • Affiliation unknown
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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) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Decision Support System for Gastrointestinal Cancer Detection Using Deep Learning</dc:title>
	<dc:creator>Aldo, Dasril</dc:creator>
	<dc:creator>Paramadini, Adanti Wido</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Convolutional Neural Network; Deep Learning; Medical Image Classification; Gastrointestinal Diseases; Clinical Decision Support Systems.</dc:subject>
	<dc:description xml:lang="en-US">Gastrointestinal diseases are health problems that require the help of medical image analysis to improve accuracy and consistency in clinical decision-making. The main challenges with multilabel classification are visual complexity and morphological similarity. The aim of this study was to develop and evaluate an in-depth learning approach to build the first gastrointestinal tract image-based Clinical Decision Support System (CDSS). This dataset is publicly available and consists of 14 classes of gastrointestinal conditions with a total of 8,750 images, including 7,000 training images and 1,750 test images with a balanced distribution of classes. Four pre-trained convolutional neural network architectures were compared, namely MobileNetV2, MobileNetV3-Small, EfficientNet-B0, and ResNet50. The evaluation metrics used were accuracy, precision, recall, F1-score, confusion matrix, and case study inference. The experimental results showed that ResNet50 outperformed the others with 88.97% accuracy, 89.13% accuracy, 88.97% recall, and 88.94% F1-score, with multiple class analyses. Single-case inference testing on six randomly selected test images obtained a confidence value between 90-99%. The selected model is integrated into the mobile CDSS app to provide a level of confidence along with the predicted outcome. This method will likely allow for fundamental image-based evaluation to be more consistent and accountable in supporting clinical decision-making.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2026-07-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/115717</dc:identifier>
	<dc:identifier>10.22146/ijccs.115717</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 20, No 3 (2026): July; 271-282</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/115717/44941</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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