The use of machine learning methods for computed tomography image classification in the Covid-19 pandemic: a review

Aug 4, 2026

COVID-19 was declared a pandemic by the World Health Organization, creating a major global health challenge. Early diagnosis based on computed tomography (CT) scans can be supported by artificial intelligence to reduce medical, logistical, and human resource burdens. This review presents the state of the art in applying machine learning to classify CT images during the COVID-19 pandemic, covering major ML method types, deep learning model construction stages such as segmentation and augmentation, and aspects of explainable AI. Results and common performance indicators are summarized, and developed models may be reused for future outbreaks or similar infectious diseases.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Introduction

Background on COVID-19 and pandemic response

Diagnostic context and challenges

Review Scope and Objectives

State of the art in CT image classification

Machine Learning and Deep Learning Methods

Types of ML methods for COVID-19 detection

Deep learning model construction stages

Explainable AI Considerations

Selected aspects of explainability

Results and Performance Indicators

Common metrics across models

Conclusion

Reuse of models for future outbreaks

Review Article The use of machine learning methods for computed tomography image classification in the Covid-19 pandemic: a review O uso de métodos de aprendizado de máquina para classificação de imagens de tomografia computadorizada na pandemia da Covid-19: uma revisão El uso de métodos de aprendizaje de máquinapara clasificación de imágenes de tomografía computarizada en lapandemia de Covid-19: una revisión Jacek Sieredziński1 Daniel Zaborski2 1109 Military Hospital and Clinic; Piotra Skargi 9-11; 70-965, Szczecin, Poland. 2Laboratory of Biostatistics, West Pomeranian University of Technology; Klemensa Janickiego 29; 71-270, Szczecin, Poland. 109 ABSTRACT Background and Objectives: Covid-19 has been declared a pandemic by the World Health Organization, representing a major challenge worldwide. An early diagnosis method for Covid- 19 is based on CT scans, which can be analyzed using artificial intelligence to save medical, logistical, and human resources. Therefore, this study aimed to present the current state of the art in the application of machine learning to classify computed tomography images in the Covid-19 pandemic. Content: The review briefly describes the types of machine learning methods for Covid- 19 detection, the stages of deep learning model construction (segmentation, augmentation), and selected aspects of explainable artificial intelligence. Finally, the application results are discussed and the most common performance indicators for individual models are given. Conclusion: Models and algorithms developed during the peak of the Covid-19 pandemic can be reused in the event of future outbreaks of this or similar infectious diseases. Keywords: Covid-19. Tomography, X-Ray Computed. Machine Learning. Deep Learning. Neural Networks, Computer. RESUMO Justificativa e Objetivos: A Covid-19 foideclarada uma pandemia pela Organização Mundialda Saúde, representando um grande desafio em todoo mundo. Um método de diagnóstico precoce da Covid-19 é baseado em tomografiascomputadorizadas, que podem ser analisadasusando inteligência artificial para economizar recursos médicos, logísticos e humanos. Portanto, o objetivo deste estudo foi apresentar o atual estado da arte na aplicação do aprendizado de máquina para classificar imagens de tomografia computadorizada na pandemia de Covid-19. Conteúdo: A revisão descreve brevemente os tipos de métodos de aprendizado de máquina paradetecção de Covid-19, os estágios de construção do modelo de aprendizagem profunda (segmentação, aumento) e aspectos selecionados da inteligência artificial explicável. Finalmente, os resultados da aplicação são discutidos e os indicadores dedesempenho mais comuns para modelos individuaissão dados. Conclusão: Modelos e algoritmos desenvolvidos durante o pico da pandemia de Covid-19 podem ser reusados no caso de futurossurtos desta ou doenças infecciosas semelhantes. Descritores: Covid-19. Tomografia Computadorizada, Raios X. Aprendizado de Máquina. Aprendizado Profundo. Redes Neurais de Computação. RESUMEN Justificación y Objetivos: La Organización Mundial de la Salud ha declarado que la Covid-19 es una pandemia, lo que ha planteó un gran desafíoa nivel mundial. Un método de diagnóstico precoz para Covid-19 se basa en tomografíascomputarizadas, que pueden analizarse mediante inteligencia artificial para ahorrar recursos médicos, logísticos y humanos. Por lo tanto, el objetivo de este estudio fue presentar el estado actual del arteen la aplicación del aprendizaje automático para clasificar imágenes de tomografía computarizada en la pandemia de Covid-19. Contenido: Larevisión describe brevemente los tipos de métodos de aprendizaje automático para la detección de Covid-19, las etapas de construcción del modelo deaprendizaje profundo (segmentación, aumento) yaspectos seleccionados de la inteligencia artificial explicable. Finalmente, se discuten los resultados de la aplicación y se presentan los indicadores derendimiento

FAQs

What is the main purpose of the review?

To present the current state of the art in applying machine learning to classify computed tomography (CT) images for COVID-19 during the pandemic.

Which deep learning construction stages are highlighted?

The review highlights stages such as segmentation and augmentation as part of deep learning model construction.

Why can the developed models be useful beyond the pandemic?

Models and algorithms created during the peak of COVID-19 can be reused in future outbreaks of COVID-19 or similar infectious diseases.

Aug 4, 2026

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