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Related Experiment Video

Updated: Jan 16, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

A novel deep neural architecture for efficient and scalable multidomain image classification.

S M Nuruzzaman Nobel^1,2, Md All Moon Tasir^3,2, Humaira Noor^4,2

¹School of Engineering, Electrical and Robotics Engineering, Monash University Malaysia, Bandar Sunway, 47500, Subang Jaya, Malaysia.

Scientific Reports

|September 26, 2025

Summary

DeepFreqNet, a novel deep neural network, enhances multi-domain image classification by combining multi-scale features, efficient convolutions, and residual connections. It achieves superior accuracy across diverse datasets, outperforming existing methods.

Keywords:

Blood cells Computer vision Deep learning Hand sign MRI tumor classification Transfer learning Vision transformer

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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Last Updated: Jan 16, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Computer Vision
Deep Learning
Artificial Intelligence

Background:

Generalizing deep learning models across diverse image domains is a significant research challenge.
Existing models often require extensive reconfiguration for new datasets, limiting their real-world applicability.

Purpose of the Study:

To introduce DeepFreqNet, a novel deep neural architecture for high-performance multi-domain image classification.
To address the limitations of current models in generalizing across varied image datasets.

Main Methods:

DeepFreqNet integrates multi-scale feature extraction, depthwise separable convolutions for efficiency, and residual connections for improved gradient flow.
The architecture is designed for seamless adaptation to diverse datasets without extensive reconfiguration, unlike traditional transfer learning approaches.

Main Results:

DeepFreqNet demonstrated superior performance on nine benchmark datasets, including MRI tumor classification, blood cell classification, and sign language recognition.
Achieved classification accuracies ranging from 98.96% to 99.97%, significantly outperforming state-of-the-art methods.

Conclusions:

DeepFreqNet offers a robust and versatile solution for real-world image classification challenges.
The novel architecture effectively learns discriminative features and scales across domains with varying data complexities.