COVIDetection: deep convolutional neural networks-based automatic detection of COVID-19 with chest x-ray images

PURPOSE: Until recently, COVID-19 was considered a highly contagious air borne infection that leads to fatal pneumonia and other health hazardous infections. The new coronavirus, or type SARS-COV-2, is responsible for COVID-19 and has demonstrated the deadly nature of the respiratory disease that threatens many people worldwide. A clinical study found that a person infected with COVID-19 can experience dry cough, muscle pain, headache, fever, sore throat, and mild to moderate respiratory illness. At the same time, it has a negative effect on the lungs if there is a viral infection. Thus, the lungs can become visible internal organs for diagnosing the severity of COVID-19 infection using chest X-rays and CT scans. Despite the long testing time, RT-PCR is a proven testing method for detecting coronavirus infection. Sometimes there are more false positives and false negatives than the desired percentage. The concept of artificial neural network (ANN) is inspired by the biological neural networks which consists of inter-connected units called artificial neurons. Convolutional neural network (CNN) which is a variant of multilayer perceptron that belongs to a class of feedforward ANN is widely used for various applications due to its enhanced accuracy. METHOD: Traditional RT-PCR methodology supports for accurate clinical diagnosis, screening for COVID-19 using an X-ray or CT scan of the human lung that can be considered. In this work, a new multi-image augmentation system is proposed based on CNN to detect COVID-19 in the chest using chest X-rays or CT images of people suspected of having the coronavirus. RESULTS: The optimal selection of slices/features has led to obtain best results for accuracy and loss. In addition to that the parameter selection reflected optimal true positive rate and false positive rates. The results look promising even with the small publicly available data set in a short period of time. CONCLUSION: This work presented a model that found to detect positive cases of COVID-19 from chest X-rays using an in-depth training model. The system demonstrates a significant performance improvement over the publicly maintained COVID-19 positive X-ray classification kit, the same dataset of pneumonia chest X-rays. The results look promising even with the small publicly available data set in terms of accuracy and loss as well as with enhanced true positive results.