ChestX-Ray6: Prediction of multiple diseases including COVID-19 from chest X-ray images using convolutional neural network

In the last few decades, several epidemic diseases have been introduced. In some cases, doctors and medical physicians are facing difficulties in identifying these diseases correctly. A machine can perform some of these identification tasks more accurately than a human if it is trained correctly. With time, the number of medical data is increasing. A machine can analyze this medical data and extract knowledge from this data, which can help doctors and medical physicians. This study proposed a lightweight convolutional neural network (CNN) named ChestX-ray6 that automatically detects pneumonia, COVID19, cardiomegaly, lung opacity, and pleural from digital chest x-ray images. Here multiple databases have been combined, containing 9,514 chest x-ray images of normal and other five diseases. The lightweight ChestX-ray6 model achieved an accuracy of 80% for the detection of six diseases. The ChestX-ray6 model has been saved and used for binary classification of normal and pneumonia patients to reveal the model's generalization power. The pre-trained ChestX-ray6 model has achieved an accuracy and recall of 97.94% and 98% for binary classification, which outweighs the state-of-the-art (SOTA) models.

Automated summary

In recent decades, epidemic diseases have posed challenges to doctors in terms of accurate identification. However, machines trained correctly can outperform humans in disease recognition. With the increasing amount of medical data, machines can analyze and extract knowledge from this data to assist doctors. In this study, a lightweight convolutional neural network named ChestX-ray6 was proposed to automatically detect pneumonia, COVID19, cardiomegaly, lung opacity, and pleural diseases from digital chest x-ray images. The ChestX-ray6 model achieved an 80% accuracy for the six diseases. Moreover, the pre-trained ChestX-ray6 model showed a superior performance compared to state-of-the-art models with a 97.94% accuracy and 98% recall in binary classification of normal and pneumonia patients.