Avci, IsaAlzabaq, Ahmed2024-09-292024-09-2920230765-00191958-5608https://doi.org/10.18280/ts.400113https://hdl.handle.net/20.500.14619/6932Convolutional Neural Network (CNN)-based deep learning techniques have recently demonstrated increased potential and effectiveness in image recognition applications, such as those involving medical images. Deep-learning models can recognize targets with performance comparable to radiologists when used with CXR. The primary goal of this research is to examine a deep learning technique used on the radiography dataset to detect COVID-19 in X-ray medical images. The proposed system consists of several stages, from pre-processing, passing through the feature reduction using more than one technique, to the classification stage based on a proposed model. The test was applied to the COVID-19 Radiography dataset of normal and three lung infections (COVID-19, Viral Pneumonia, and Lung Opacity). The proposed CNN model has shown its ability to classify COVID, normal, and other lung infections with perfect accuracy of 99.94%. Consequently, the AI-based early-stage detection algorithms will be enhanced, increasing the accuracy of the X-ray -based modality for the screening of various lung diseases.eninfo:eu-repo/semantics/openAccessCOVID-19 convolutional neural network(CNN) linear discriminant analysis (LDA)gray level co-occurrence matrix (GLCM)radiographyArticle10.18280/ts.4001132-s2.0-851521944501551Q314540WOS:000957612200013Q4