Automated detection of COVID-19 from X-ray images using CNN and Android mobile

Bushra, Kanij Fatema; Ahamed, Md. Asif; Ahmad, Mohiuddin

PURPOSE: The prevalence of the coronavirus disease 2019 (COVID-19) pandemic has made a huge impact on global health and the world economy. Easy detection of COVID-19 through any technological tool like a mobile phone can help a lot. In this research, we focus on detecting COVID-19 from X-ray images on Android mobile with the help of Artificial Intelligence (AI). METHODS: A convolutional neural network (CNN) model is developed in MATLAB and then converted to the CNN model to TensorFlow Lite (TFLite) model to deploy on Android mobile. An Android application is developed which uses the TFLite model to detect COVID-19 using X-ray images. RESULTS: By employing a 5-fold cross-validation, an average accuracy of 98.65%, sensitivity of 98.49%, specificity of 98.82%, precision of 98.81%, and F1-score of 98.65% are achieved in COVID-19 detection. CONCLUSION: With our developed Android application, users can detect COVID-19 from X-ray images on Android mobile, and it will be helpful for the diagnosis of COVID-19.