Decision Support System for Gastrointestinal Cancer Detection Using Deep Learning - Aldo, Dasril; Paramadini, Adanti Wido (IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2026)
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.