CAE-COVIDX: automatic covid-19 disease detection based on x-ray images using enhanced deep convolutional and autoencoder - Hanafi Hanafi; Andri Pranolo; Yingchi Mao (International Journal of Advances in Intelligent Informatics, 2021)
Since the first case in 2019, Corona Virus has been spreading all over the world. World Health Organization (WHO) announced that COVID-19 had become an international pandemic. There is an essential section to handle the spreading of the virus by immediate virus detection for patients. Traditional medical detection requires a long time, a specific laboratory, and a high cost. A method for detecting Covid-19 faster compared to common approaches, such as RT-PCR detection, is needed. The method demonstrated that it could produce an X-ray image with higher accuracy and consumed less time. We propose a novel method to extract image features and to classify COVID-19 using deep CNN combined with Autoencoder (AE) dubbed CAE-COVIDX. We evaluated and compared it with the traditional CNN and existing framework VGG16 involving 400 normal images of non-COVID19 and 400 positive COVID-19 diseases. The performance evaluation was conducted using accuracy, confusion matrix, and loss evaluation. Based on experiment results, the CAE-COVIDX framework outperforms previous methods in several testing scenarios. This framework's ability to detect Covid-19 in various nonstandard image X-rays could effectively help medical employers diagnose Covid-19 patients. It is an important factor to decrease the spreading of Covid-19 massively.