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Updated: Sep 6, 2025
13:19
Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
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Uncertainty handling in convolutional neural networks.
Elyas Rashno1, Ahmad Akbari1, Babak Nasersharif2
1Department of Computer Engineering, Iran University of Science and Technology, Narmak, Tehran, 1684613114 Iran.
Neural Computing & Applications
|June 27, 2022
Summary
Neutrosophic Convolutional Neural Networks (NCNN) improve image classification accuracy on noisy data. This novel approach enhances Convolutional Neural Networks (CNN) and VGG-Net performance by handling data indeterminacy in the neutrosophic domain.
Area of Science:
- Computer Science
- Artificial Intelligence
- Image Processing
Background:
- Convolutional Neural Networks (CNNs) performance degrades with noisy data, particularly during testing.
- Existing methods struggle to effectively mitigate the impact of data indeterminacy in image classification tasks.
Purpose of the Study:
- To propose a novel Neutrosophic Convolutional Neural Network (NCNN) structure for robust image classification.
- To address the challenge of noisy data and data indeterminacy in neural network performance.
Main Methods:
- Images are mapped from the pixel domain to the neutrosophic (NS) domain (True, Indeterminacy, False sets).
- A two-path NCNN architecture is constructed, processing True and Indeterminacy sets in parallel.
- Weights are updated using backpropagation, and the NS concept is applied to CNN and VGG-Net.
Main Results:
- NCNN outperforms standard CNN by 5.11% and 2.21% on noisy MNIST and CIFAR-10 datasets.
- NVGG-Net shows accuracy improvements of 3.09% and 2.57% over VGG-Net on CIFAR-10 and CIFAR-100 datasets.
- The proposed method demonstrates significant robustness against various levels of Gaussian noise.
Conclusions:
- The two-path NCNN effectively handles noisy data and data indeterminacy, outperforming traditional CNNs.
- The neutrosophic approach offers a promising direction for enhancing the resilience of deep learning models in image classification.
- NCNN and NVGG-Net provide improved accuracy and robustness, especially in the presence of significant data noise.
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