Abstract
Artificial intelligence (AI) techniques in general and convolutional neural networks (CNNs) in particular have attained successful results in medical image analysis and classification. A deep CNN architecture has been proposed in this paper for the diagnosis of COVID-19 based on the chest X-ray image classification. Due to the nonavailability of sufficient-size and good-quality chest X-ray image dataset, an effective and accurate CNN classification was a challenge. To deal with these complexities such as the availability of a very-small-sized and imbalanced dataset with image-quality issues, the dataset has been preprocessed in different phases using different techniques to achieve an effective training dataset for the proposed CNN model to attain its best performance. The preprocessing stages of the datasets performed in this study include dataset balancing, medical experts’ image analysis, and data augmentation. The experimental results have shown the overall accuracy as high as 99.5% which demonstrates the good capability of the proposed CNN model in the current application domain. The CNN model has been tested in two scenarios. In the first scenario, the model has been tested using the 100 X-ray images of the original processed dataset which achieved an accuracy of 100%. In the second scenario, the model has been tested using an independent dataset of COVID-19 X-ray images. The performance in this test scenario was as high as 99.5%. To further prove that the proposed model outperforms other models, a comparative analysis has been done with some of the machine learning algorithms. The proposed model has outperformed all the models generally and specifically when the model testing was done using an independent testing set.
Highlights
Aijaz Ahmad Reshi,1 Furqan Rustam,2 Arif Mehmood,3 Abdulaziz Alhossan,4,5 Ziyad Alrabiah,4 Ajaz Ahmad,4 Hessa Alsuwailem,4 and Gyu Sang Choi 6
Due to the nonavailability of sufficient-size and good-quality chest X-ray image dataset, an effective and accurate convolutional neural networks (CNNs) classification was a challenge. To deal with these complexities such as the availability of a very-small-sized and imbalanced dataset with image-quality issues, the dataset has been preprocessed in different phases using different techniques to achieve an effective training dataset for the proposed CNN model to attain its best performance. e preprocessing stages of the datasets performed in this study include dataset balancing, medical experts’ image analysis, and data augmentation. e experimental results have shown the overall accuracy as high as 99.5% which demonstrates the good capability of the proposed CNN model in the current application domain. e CNN model has been tested in two scenarios
Chest X-ray is used to diagnose the chest-related diseases like pneumonia and other lung diseases [4], as it provides the image of the thoracic cavity, consisting of the chest and spine bones along with the soft organs including the lungs, blood vessels, and airways. e X-ray imaging technique provides numerous advantages as an alternative diagnosis procedure for COVID-19 over other testing procedures. ese benefits include its low cost, the vast availability of X-ray facilities, noninvasiveness, less time consumption, and device affordability. us, X-ray imaging may be considered a better candidate for the mass, easy, and quick diagnosis procedure for a pandemic like COVID-19 considering the current global healthcare crisis
Summary
Deep learning has shown a dramatic increase in the medical applications in general and in medical imagebased diagnosis. In this study, tailored deep CNN design has been reported for the detection of COVID-19 patients using X-ray images. Transfer learning has achieved a promising accuracy of 97.82% in COVID-19 detection in this study Another recent and relevant study has been conducted on validation and adaptability of Decompose-, Transfer-, and Compose-type deep CNN for COVID-19 detection using chest X-ray image classification [21]. Having reviewed the relevant and recent research work on the design, development, and possible applicability of CNNs in COVID-19 detection using medical images, X-ray images, due to the availability of a very less amount of X-ray images of COVID-19 patients and the poor quality of some images in the dataset, the accuracy of the models was affected. Having reviewed the relevant and recent research work on the design, development, and possible applicability of CNNs in COVID-19 detection using medical images, X-ray images, due to the availability of a very less amount of X-ray images of COVID-19 patients and the poor quality of some images in the dataset, the accuracy of the models was affected. is study is focused on dataset preprocessing to fine-tune it, data augmentation, and design of a CNN with extra layers to increase further the performance of the COVID-19 diagnosis using CNNs as described in subsequent sections
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