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Updated: Sep 22, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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A Deep Learning Framework Integrating the Spectral and Spatial Features for Image-Assisted Medical Diagnostics.

Susmita Ghosh1, Swagatam Das1, Rammohan Mallipeddi2

  • 1Electronics and Communication Sciences UnitIndian Statistical Institute Kolkata 700108 India.

IEEE Access : Practical Innovations, Open Solutions
|May 18, 2022
PubMed
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This study introduces a novel computer-aided diagnostic system for COVID-19 detection using chest X-rays. Combining spatial and spectral image features significantly improves disease detection accuracy.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Manual disease diagnosis is time-consuming and prone to errors.
  • The COVID-19 pandemic highlighted the need for rapid, automated diagnostic tools.
  • Existing methods often overlook the spectral information within medical images.

Purpose of the Study:

  • To develop an automated diagnostic system for COVID-19 detection using chest radiographs.
  • To investigate the utility of integrating spatial and spectral features for enhanced disease detection.
  • To create a robust and generalizable medical image analysis solution.

Main Methods:

  • Chest radiographs were analyzed using a three-stage approach: feature extraction, dimensionality reduction, and prediction.
Keywords:
COVID-19 detectionMedical imagingclass imbalancedeep learningdiagnostic solutiondiscrete cosine transformdiscrete wavelet transformsaliency map

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  • Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) were employed for spectral and spatio-spectral feature extraction.
  • Convolutional Neural Networks (CNN) reduced feature dimensions, followed by Multilayer Perceptron (MLP) for classification.

    • Main Results:

    • The integrated approach combining spatial, spectral, and spatio-spectral features outperformed methods using individual feature types.
    • Saliency maps confirmed the reliability of the model in identifying disease-specific patterns.
    • The method demonstrated effectiveness across diverse medical imaging datasets for various conditions.

    Conclusions:

    • Integrating spectral and spatial features offers complementary information for improved medical image analysis.
    • The proposed system shows potential as a generalized and robust tool for computer-aided medical diagnosis.
    • This approach can aid in easing the manual diagnostic process and improving healthcare efficiency.