# Performance Analysis for COVID-19 Diagnosis Using Custom and State-of-the-Art Deep Learning Models
## 6 Conclusions  
Artificial intelligence was used to deploy experimentation on a dataset containing the largest number of COVID-19 images ever used, to the best of the authors' knowledge and based on a survey of the literature. For the subject purpose, a CNN model was created and deployed, and state-of-the-art deep learning models were selected, including one which achieved top performance in a previous study. The main purpose was to test the performance of deep learning algorithms on the updated COVID-19 database, and also to develop and test a custom deep learning model on the subject dataset. However, there was no intention to develop a highly remarkable deep learning model, but rather to test the performance on a relatively larger database of chest X-ray images. Moreover, the intention was to determine the performance gains on the utilized, larger COVID-19 chest X-ray image dataset.  
The maximum performance from among the deployed models on the subject dataset was 94.206% in terms of accuracy, 99% in terms of precision, 95% in terms of recall, and 97% in terms of F1 score. Promising results were found for deep learning when deployed for the diagnosis of COVID-19 using chest X-rays. In terms of precision, it was found that 99% of the predicted positive COVID-19 cases were accurate, which is of high scientific value. Furthermore, the experimentation on the subject dataset showed that a higher accuracy in precision is obtained when compared with recall, demonstrating that the percentage of correctly identified positive cases than the percentage of total correctly identified COVID-19 cases out of overall positive COVID-19 cases in the dataset. The MobileNetV2 model succeeded in predicting slightly more cases of COVID-19 correctly, while producing far more wrong predictions of COVID-19 cases. Also, the MobileNetV2 model gave slightly lower misclassified COVID-19 cases when compared with Xception. In terms of accuracy, the current evaluation shows a decrease of 2% in accuracy when compared with the average accuracy of covered literature work for 3-class classification. Although this study offers a detailed and thorough insight into the performance results of a larger COVID-19 chest X-ray image dataset, we, nevertheless, believe that the performance analysis of deep learning models on COVID-19 will become increasingly mature with a competitive ratio of analyzed subject virus chest X-ray images to other, normal, and abnormal images in the dataset. It is essential to mention here that although we utilized a large database with the highest number of chest X-ray images of the subject virus, we still had a data imbalance where COVID-19 chest X-ray images formed 18% of the total images, and lung opacity and normal cases formed 30% and 52%, respectively.  
Conceptualization, investigation, methodology, resources, software, and writing-original draft: A.T.N., M.J.A. and O.T.; investigation, validation, review and editing: A.T.N., M.J.A. and O.T.; conceptualization, software, writing--review and editing: [PERSON], [PERSON] ([PERSON]) and [PERSON] ([PERSON]); writing--review and editing A.T.N., [PERSON], [PERSON], [PERSON] ([PERSON]) and [PERSON] All authors have read and agreed to the published version of the manuscript.  
The authors received no specific funding for this study.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** This dataset available online and anyone can be used.  
**Acknowledgments:** This research work was partially supported by Chiang Mai University and University of Management and Technology.  
**Conflicts of Interest:** The authors declare no conflict of interest.