02517nas a2200289   4500000000100000000000100001008004100002260001200043653002500055653002100080653003900101653001000140653001000150653001200160100002700172700002200199700001300221700001700234700001700251700001900268245013200287856008000419300001000499490000600509520169800515022001402213       2021                            d  c06/202110aCoronavirus COVID-1910aMachine Learning10aConvolutional Neural Network (CNN)10aX-ray10aImage10aCT Scan1 aMuhammad Irfan Khattak1 aMu’ath Al-Hasan1 aAtif Jan1 aNasir Saleem1 aElena Verdú1 aNuman Khurshid00aAutomated Detection of COVID-19 using Chest X-Ray Images and CT Scans through Multilayer- Spatial Convolutional Neural Networks  uhttps://www.ijimai.org/journal/sites/default/files/2021-05/ijimai_6_6_2.pdf  a15-240 v63 aThe novel coronavirus-2019 (Covid-19), a contagious disease became a pandemic and has caused overwhelming effects on the human lives and world economy. The detection of the contagious disease is vital to avert further spread and to promptly treat the infected people. The need of automated scientific assisting diagnostic methods to identify Covid-19 in the infected people has increased since less accurate automated diagnostic methods are available. Recent studies based on the radiology imaging suggested that the imaging patterns on X-ray images and Computed Tomography (CT) scans contain leading information about Covid-19 and is considered as a potential automated diagnosis method. Machine learning and deep learning techniques combined with radiology imaging can be helpful for accurate detection of the disease. A deep learning approach based on the multilayer-Spatial Convolutional Neural Network for automatic detection of Covid-19 using chest X-ray images and CT scans is proposed in this paper. The proposed model, named as the Multilayer Spatial Covid Convolutional Neural Network (MSCovCNN), provides an automated accurate diagnostics for Covid-19 detection. The proposed model showed 93.63% detection accuracy and 97.88% AUC (Area Under Curve) for chest x-ray images and 91.44% detection accuracy and 95.92% AUC for chest CT scans, respectively. We have used 5-tiered 2D-CNN frameworks followed by the Artificial Neural Network (ANN) and softmax classifier. In the CNN each convolution layer is followed by an activation function and a Maxpooling layer. The proposed model can be used to assist the radiologists in detecting the Covid-19 and confirming their initial screening.  a1989-1660