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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 © 2021, Universitas Ahmad Dahlan All rights reserved

CAE-COVIDX: automatic covid-19 disease detection based on x-ray images using enhanced deep convolutional and autoencoder