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Related Experiment Video

Updated: Jan 9, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.9K

Block-Level Genetic Algorithm Optimization for ResNet Endoscopic Image Classification.

Gilberto R De Souza Junior, Gilberto F De Sousa Filho, Lucidio Dos Anjos F Cabral

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

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    This study optimized ResNet-152 for medical image analysis using a genetic algorithm, achieving high accuracy. The research offers insights into model interpretability for endoscopic images, crucial for clinical trust.

    Area of Science:

    • Artificial Intelligence
    • Medical Imaging
    • Computer Vision

    Background:

    • Convolutional Neural Networks (CNNs) excel at medical image pattern recognition.
    • Explainability is crucial for high-risk medical AI applications.
    • ResNet-152 is a powerful CNN architecture.

    Purpose of the Study:

    • To enhance ResNet-152 performance on medical images using optimization.
    • To investigate model interpretability in endoscopic imaging.
    • To improve clinical acceptance of AI in endoscopy.

    Main Methods:

    • Implemented a Neural Architecture Search framework with a Genetic Algorithm.
    • Optimized the ResNet-152 architecture.
    • Applied preprocessing and selective block optimization on the Kvasir v2 dataset.

    Related Experiment Videos

    Last Updated: Jan 9, 2026

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.9K

    Main Results:

    • Achieved 97.75% accuracy on the validation set and 97.12% on the test set.
    • Identified specific bottleneck blocks as structurally significant for dataset features.
    • Provided insights into ResNet-152's behavior with endoscopic images.

    Conclusions:

    • The optimization framework enhances ResNet-152 performance and interpretability.
    • Findings contribute to explainable AI in medical imaging, particularly for endoscopy.
    • This work supports the integration of interpretable AI for clinical decision-making.