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Updated: Sep 25, 2025

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Multiclass Convolution Neural Network for Classification of COVID-19 CT Images.

Serena Low Woan Ching1, Khin Wee Lai2, Joon Huang Chuah1

  • 1Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia.

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|May 2, 2022
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Summary
This summary is machine-generated.

This study introduces transfer learning with deterministic algorithms for COVID-19 classification using CT scans. ResNeXt101 and ResNet152 models demonstrated high accuracy and F1 scores, aiding in distinguishing COVID-19 from other conditions.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Diseases

Background:

  • The novel coronavirus (COVID-19) pandemic presents diagnostic challenges due to symptom overlap with other respiratory illnesses.
  • Accurate and rapid classification of COVID-19 is crucial for effective patient management and public health.
  • Computed Tomography (CT) imaging shows promise for COVID-19 diagnosis, but automated analysis requires robust models.

Purpose of the Study:

  • To evaluate the effectiveness of transfer learning with deterministic algorithms for classifying COVID-19 using CT images.
  • To compare the performance of various Convolutional Neural Network (CNN) architectures in identifying COVID-19 positive and negative cases.
  • To identify the optimal CNN models for accurate COVID-19 detection from CT scans.

Main Methods:

  • A dataset of 746 CT images (COVID-19 and non-COVID-19) was utilized, split into training, validation, and testing sets.
  • Data augmentation techniques were applied to enhance the training dataset size.
  • Transfer learning with deterministic algorithms was implemented, and CNN models (ResNeXt101, ResNet152, GoogleNet, DenseNet201, ResNet101) were pretrained for binary classification.

Main Results:

  • ResNeXt101 achieved the highest F1 score (0.978) and accuracy (97.81%), along with 95.71% sensitivity and 100% specificity.
  • ResNet152 demonstrated strong performance with an F1 score of 0.938, accuracy of 93.80%, 90% sensitivity, and 98.33% specificity.
  • GoogleNet yielded a lower F1 score of 0.762, indicating varying performance across different CNN architectures.

Conclusions:

  • Transfer learning combined with deterministic algorithms and CNNs offers a highly effective approach for COVID-19 classification from CT images.
  • Models like ResNeXt101 and ResNet152 show significant potential for accurate and reliable COVID-19 detection in clinical settings.
  • The study highlights the importance of selecting appropriate CNN architectures for optimal diagnostic performance in medical image analysis.