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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
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COVID-19 detection using X-ray images and statistical measurements.
1Department of Software Engineering, Faculty of Engineering, Aksaray University, Aksaray TURKEY.
Measurement : Journal of the International Measurement Confederation
|August 9, 2022
Summary
This study introduces a machine learning approach for early COVID-19 diagnosis using chest X-ray images. The system achieved high accuracy in identifying pneumonia, aiding healthcare professionals during the pandemic.
Area of Science:
- Medical Imaging
- Machine Learning
- Computational Biology
Background:
- The COVID-19 pandemic posed a significant global health threat, overwhelming healthcare systems.
- Accurate and timely diagnosis of COVID-19, particularly pneumonia, is crucial for patient management and preventing disease progression.
- Machine learning offers potential for developing computer-aided systems to alleviate diagnostic burdens on healthcare professionals.
Purpose of the Study:
- To develop and evaluate a machine learning model for the early and accurate diagnosis of pneumonia using medical images.
- To assess the efficacy of a novel image preprocessing technique in enhancing diagnostic accuracy.
- To compare the performance of five different machine learning algorithms for pneumonia detection.
Main Methods:
- A dataset of pneumonia and normal chest X-ray images was utilized.
- A new image preprocessing method was applied, followed by feature extraction based on RGB values.
- Images were resized to 15x15 units for feature reduction and analysis.
- Five machine learning algorithms were employed: Multi-Class Support Vector Machine (MC-SVM), k-Nearest Neighbor (k-NN), Decision Tree (DT), Multinomial Logistic Regression (MLR), and Naive Bayes (NB).
Main Results:
- Training accuracy rates for the algorithms were as follows: MC-SVM (1), k-NN (1), DT (1), MLR (0.746), and NB (0.964).
- Testing accuracy rates were: MC-SVM (0.878), k-NN (0.857), DT (0.857), MLR (0.878), and NB (0.939).
- The Naive Bayes algorithm demonstrated the highest accuracy in the test set (0.939).
Conclusions:
- The proposed machine learning model, particularly with the Naive Bayes algorithm, shows promise for accurate and early detection of pneumonia.
- The image preprocessing technique and feature extraction method are effective in improving diagnostic performance.
- Computer-aided diagnosis systems can significantly support healthcare professionals in managing infectious disease outbreaks like COVID-19.
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