• Venkata Reddy Bhimireddy

Deep Learning techniques in general and convolutional neural networks (CNNs) in particular have gained successful results in medical picture analysis and categorization. A deep CNN architecture has been developed in this work for the diagnosis of COVID-19 based on the chest X-ray image categorization. Due to the nonavailability of sufficient-size and good-quality chest X-ray image dataset, an effective and reliable 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 testing results have showed the overall accuracy as high as 98.6 percent which illustrates the good capability of the suggested CNN model in the current application area. The CNN model has been evaluated in two scenarios. In the first scenario, the model has been tested using the 94 X-ray pictures of the original processed dataset which achieved an accuracy of 100 percent . In the second case, the model has been tested using an independent dataset of COVID-19 X-ray pictures. The performance in this test scenario was as high as 98.6 percent . To further prove that the suggested model outperforms existing models, a comparative analysis has been done with some of the machine learning methods. The proposed model has outperformed all the models generally and specifically when the model testing was done using an independent testing set. However, a larger dataset of COVID-19 X-ray images is necessary for a more accurate and trustworthy detection of COVID-19 infections when utilising deep transfer learning. This would be highly advantageous in this pandemic when the illness burden and the demand for preventative actions are in contradiction with the existing available resources.

Covid-19 Detection and Visualization

"Covid-19 Detection and Visualization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.f182-f188, June-2022, Available :http://www.jetir.org/papers/JETIR2206524.pdf

"Covid-19 Detection and Visualization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppf182-f188, June-2022, Available at : http://www.jetir.org/papers/JETIR2206524.pdf