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Updated: May 2, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Opt Deep CSSAN: Optimized Deep Convolutional Spectral-Spatial Attention Network for hyperspectral image

Nisha A¹, A Anitha²

¹Computer Applications, Noorul Islam Centre for Higher Education, Kumaracoil, Kanyakumari District, Tamil Nadu 629 180, India.

Computational Biology and Chemistry

|December 19, 2025

Summary

This summary is machine-generated.

This study introduces a novel deep learning method for hyperspectral image classification (HSIC). The proposed approach achieves high accuracy, outperforming standard techniques in agriculture, geology, and security applications.

Keywords:

Hyperspectral image classification deep learning feature extraction feature selection

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Area of Science:

Remote Sensing
Computer Vision
Data Science

Background:

Hyperspectral imaging provides detailed topographic data crucial for applications like agriculture, geology, and national security.
Hyperspectral image classification (HSIC) is a key challenge, with deep learning showing significant promise for feature extraction.

Purpose of the Study:

To develop an advanced deep learning framework for improved Hyperspectral Image Classification (HSIC).
To integrate band selection, feature extraction, dimension reduction, and classification into a unified model.

Main Methods:

Band selection using Double Exponential Smoothing-Artificial Flora Optimization (DES-AFO).
Feature extraction via Empirical Wavelet Transform (EWT), Convolutional Neural Network (CNN), and ResNet50.
Dimension reduction using Canonical Correlation Analysis (CCA).
Classification using an Optimized Deep Convolutional Spectral-Spatial Attention Network (Opt Deep CSSAN), trained with DES-AFO.

Main Results:

The DES-AFO based Opt Deep CSSAN achieved superior performance compared to existing methods.
Achieved 96.9% accuracy, 97.1% True Positive Rate (TPR), 95.8% Kappa, 96.9% True Negative Rate (TNR), and 91.5% Positive Predictive Value (PPV).

Conclusions:

The proposed integrated deep learning framework significantly enhances hyperspectral image classification accuracy.
This method offers a robust solution for diverse applications requiring precise hyperspectral data analysis.