Early Diagnosis of COVID-19 Disease by ChestNet Convolutional Neural Network from Chest Xray Images

In the absence of a vaccine, prompt isolation and medical care are necessary to prevent and contain the COVID-19 pandemic. This requires a quick and precise identification of the virus. The rising incidence of COVID-19 cases reported globally, along with the scarcity of detection tests, make it challenging to diagnose the illness. It is therefore necessary to search for other options at this moment. While X-rays are a commonly utilised form of imaging among readily available, affordable, and broadly spread resources, deep learning approaches have attained advanced capabilities in automated diagnosis in medicine. Thus, this work proposes an ChestNet Convolutional Neural Network, alternate method to identify COVID-19 instances using cutting-edge computational intelligence algorithms. This work consists of three main stages including data pre-processing stage, data enhancement stage as well as classification stage. The performance of the proposed work is evaluated on the Chest X-ray images obtained from Kaggle repository.