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Classification by a stacking model using CNN features for COVID-19 infection diagnosis.

Yavuz Selim Taspinar¹, Ilkay Cinar², Murat Koklu²

¹Doganhisar Vocational School, Selcuk University, Konya, Turkey.

Journal of X-Ray Science and Technology

|November 1, 2021

Summary

Machine learning models accurately classify COVID-19 pneumonia from chest X-rays. A stacking model achieved 96.9% accuracy, offering a fast, inexpensive tool for clinical diagnosis support.

Keywords:

COVID-19 Convolutional neural network Stacking model X-ray chest images

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Area of Science:

Medical Imaging
Machine Learning
Infectious Diseases

Background:

COVID-19 pandemic has caused millions of deaths globally.
Survivors may develop severe pneumonia, leading to organ failure and death.
Accurate and timely diagnosis is critical for patient outcomes.

Purpose of the Study:

To classify chest X-ray images into COVID-19, normal, and viral pneumonia categories.
To evaluate the performance of machine learning models for this classification task.
To develop an efficient diagnostic aid for COVID-19.

Main Methods:

Utilized a dataset of 3486 chest X-ray images.
Trained and compared three single machine learning models: Support Vector Machine (SVM), Logistics Regression (LR), and Artificial Neural Network (ANN).
Developed and evaluated a stacking model combining the three single models.

Main Results:

The SVM, ANN, and LR models achieved accuracies of 90.2%, 96.2%, and 96.7%, respectively.
The proposed stacking model achieved a classification accuracy of 96.9%.
Performance metrics including recall, precision, and F-1 score were computed for all models.

Conclusions:

The developed stacking model demonstrates high accuracy in classifying COVID-19 pneumonia from chest X-rays.
This machine learning approach provides a fast, inexpensive, and effective method for assisting in COVID-19 diagnosis.
The model has the potential to enhance diagnostic efficiency for healthcare professionals in busy clinical settings.