The usefulness of the modified deep convolutional neural network model in improving the detection of COVID-19 on chest X-ray images
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Abstract
Background: The COVID-19 pandemic has rapidly spread worldwide, leading to a global health crisis. Although the real-time polymerase chain reaction (RT-PCR) test is highly specific and sensitive in detecting COVID-19, chest X-rays have emerged as an optional diagnostic tool for COVID-19-induced lung lesions. Artificial intelligence (AI), particularly deep learning, is a rapidly evolving field with significant potential in medical image analysis, including the quick detection of COVID-19 to improve accuracy.
Objectives: This study aims to enhance the accuracy of COVID-19 image detection on chest X-ray images by modifying the deep convolutional neural network.
Materials and methods: We conducted lung segmentation and COVID-19 image classification experiments using a dataset of chest X-rays. The U-net algorithm was utilized for lung segmentation of COVID-19 and non-COVID-19 images. We developed a Modified Deep Convolutional Neural Network (MD-CNN) to classify the two image classes. The MD-CNN model was compared with two other models, ResNet and AlexNet, and evaluated for accuracy, sensitivity (recall), specificity, positive predictive value (precision), F1-score, and area under the curve (AUC).
Results: Our experimental results demonstrate that the MD-CNN model achieved an accuracy of 97.95%, outperforming ResNet and AlexNet, which achieved 90.25% and 78.95%, respectively. The MD-CNN model also exhibited better sensitivity, F1-score, and AUC than the other models, while its specificity and precision were comparable to those of the ResNet model.
Conclusion: The proposed MD-CNN model demonstrates significant potential for high accuracy in COVID-19 image detection compared to ResNet and AlexNet. It can serve as a useful tool for radiologists in the COVID-19 screening process, potentially reducing the workload, and improving the efficiency of COVID-19 diagnosis.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Personal views expressed by the contributors in their articles are not necessarily those of the Journal of Associated Medical Sciences, Faculty of Associated Medical Sciences, Chiang Mai University.
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