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Related Experiment Video

Updated: Aug 6, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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Comparative study of convolutional neural network architectures for gastrointestinal lesions classification.

Erik O Cuevas-Rodriguez1, Carlos E Galvan-Tejada1, Valeria Maeda-Gutiérrez1

  • 1Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas, Zacatecas, México.

Peerj
|March 22, 2023
PubMed
Summary

Deep learning models, specifically convolutional neural networks (CNNs), show promise in detecting gastrointestinal (GI) tract lesions from endoscopic images. DenseNet-201 achieved the highest accuracy, improving diagnostic potential.

Keywords:
ClassificationComputer-aided diagnosticConvolutional neural networkDeep learningEndoscopyGastrointestinalGastrointestinal lesions

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Gastroenterology
  • Computational Pathology

Background:

  • Gastrointestinal (GI) diseases like esophagitis, ulcers, and polyps require accurate detection via endoscopy.
  • Endoscopic diagnosis relies heavily on physician expertise, leading to potential missed findings.
  • Deep learning (DL) offers a solution to enhance diagnostic accuracy in endoscopic imaging.

Purpose of the Study:

  • To compare the performance of four distinct convolutional neural network (CNN) architectures for classifying GI tract lesions.
  • To identify the most effective CNN architecture for automated detection of GI abnormalities in endoscopic images.

Main Methods:

  • Four CNN architectures (AlexNet, DenseNet-201, Inception-v3, ResNet-101) were evaluated.
  • The HyperKvasir dataset, comprising 6,792 endoscopic images of 10 findings, was utilized.
  • Transfer learning and data augmentation techniques were applied during model training and testing.

Main Results:

  • DenseNet-201 demonstrated superior performance, achieving 97.11% accuracy.
  • Key performance metrics for DenseNet-201 included 96.3% sensitivity, 99.67% specificity, and 95% AUC.
  • The study successfully validated the efficacy of DL in classifying diverse GI tract lesions.

Conclusions:

  • DenseNet-201 is highly effective for automated classification of GI tract lesions from endoscopic images.
  • DL-based approaches, particularly using advanced CNNs, can significantly aid gastroenterologists in lesion detection.
  • This technology holds potential to reduce diagnostic errors and improve patient outcomes in GI endoscopy.