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Related Experiment Video

Updated: May 21, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Segnet unveiled: Robust image segmentation via rigorous K-fold cross-validation analysis.

Ignatious K Pious¹, R Srinivasan¹

¹Department of Computer Science and Engineering, Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Chennai, India.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine

|March 19, 2025

Summary

The SegNet model demonstrates reliable image segmentation performance across diverse datasets, achieving high Dice Coefficient and Intersection over Union (IOU) scores. K-fold cross-validation confirms its generalization capabilities for various applications.

Keywords:

K-fold cross-validation SegNet model accuracy computer vision dice coefficient image segmentation intersection over union optimization precision recall

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Last Updated: May 21, 2025

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Deep Neural Networks for Image-Based Dietary Assessment

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Area of Science:

Computer Vision
Machine Learning

Background:

Image segmentation is a fundamental task in computer vision.
Applications span autonomous driving and medical imaging.

Purpose of the Study:

To evaluate the performance of a SegNet-based image segmentation model.
To ensure reliable segmentation across varied datasets.

Main Methods:

Utilized five-fold and K-fold cross-validation for model assessment.
Measured Intersection over Union (IOU), Dice Coefficient, Precision, Recall, Accuracy, and loss metrics.

Main Results:

Consistently high performance with Dice Coefficients from 88.32% to 89.8%.
Intersection over Union (IOU) scores ranged from 94.53% to 95.05%.
Precision, Recall, and Accuracy metrics frequently exceeded 90%, indicating model dependability.

Conclusions:

K-fold cross-validation enhances validation reliability for the SegNet model.
The model demonstrates strong generalization across datasets, suitable for practical image segmentation tasks.