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Related Experiment Video

Updated: Aug 27, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Covid-19 classification using sigmoid based hyper-parameter modified DNN for CT scans and chest X-rays.

B Anilkumar1, K Srividya2, A Mary Sowjanya3

  • 1Department of ECE, GMR Institute of Technology, Rajam, India.

Multimedia Tools and Applications
|September 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep neural network (DNN) method for classifying COVID-19, Pneumonia, and normal cases from CT and CXR images. The advanced SHMDNN model achieved a 99.9% accuracy, improving diagnostic capabilities for respiratory illnesses.

Keywords:
AGWOCovid-19DNNGaussian filterPre-processingSigmoid value

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Infectious Disease Diagnostics

Background:

  • Coronavirus disease (COVID-19) diagnosis using Computed Tomography (CT) and Chest X-rays (CXR) faces challenges like overfitting and early detection.
  • Accurate and timely classification of respiratory conditions is crucial for effective patient management.

Purpose of the Study:

  • To develop and evaluate a novel deep neural network (DNN) model for classifying COVID-19, Pneumonia, and normal cases from CT and CXR images.
  • To enhance diagnostic accuracy and address limitations in current imaging-based detection methods.

Main Methods:

  • Image pre-processing using an adaptive Gaussian filter for noise reduction.
  • Implementation of a Sigmoid Based Hyper-Parameter Modified DNN (SHMDNN) model.
  • Hyperparameter optimization utilizing the Adaptive Grey Wolf Optimization (AGWO) algorithm.

Main Results:

  • The SHMDNN model successfully classified CT and CXR images into three categories: normal, Pneumonia, and COVID-19.
  • Achieved a high classification accuracy of 99.9%.
  • Demonstrated superior performance compared to other deep neural network architectures.

Conclusions:

  • The proposed SHMDNN model with AGWO optimization offers a highly accurate and effective approach for diagnosing COVID-19 and Pneumonia from medical images.
  • This method shows significant potential for improving early and accurate diagnosis in clinical settings.