Tekerek Adem, Al-Rawe Ismael Abdullah Mohammed
Department of Computer Engineering, Technology Faculty, Gazi University, Ankara, Türkiye.
Wirel Pers Commun. 2023 May 12:1-15. doi: 10.1007/s11277-023-10489-y.
Covid19 corona virus has caused widespread disruption across the world, in terms of the health, economy, and society problems. X-ray images of the chest can be helpful in making an accurate diagnosis because the corona virus typically first manifests its symptoms in patients' lungs. In this study, a classification method based on deep learning is proposed as a means of identifying lung disease from chest X-ray images. In the proposed study, the detection of covid19 corona virus disease from chest X-ray images was made with MobileNet and Densenet models, which are deep learning methods. Several different use cases can be built with the help of MobileNet model and case modelling approach is utilized to achieve 96% accuracy and an Area Under Curve (AUC) value of 94%. According to the result, the proposed method may be able to more accurately identify the signs of an impurity from dataset of chest X-ray images. This research also compares various performance parameters such as precision, recall and F1-Score.
新冠病毒已在全球范围内造成广泛破坏，涉及健康、经济和社会问题。胸部X光图像有助于做出准确诊断，因为新冠病毒通常首先在患者肺部表现出症状。在本研究中，提出了一种基于深度学习的分类方法，作为从胸部X光图像中识别肺部疾病的手段。在所提出的研究中，使用深度学习方法MobileNet和Densenet模型从胸部X光图像中检测新冠病毒疾病。借助MobileNet模型可以构建几个不同的用例，并利用案例建模方法实现了96%的准确率和94%的曲线下面积（AUC）值。根据结果，所提出的方法可能能够从胸部X光图像数据集中更准确地识别病变迹象。本研究还比较了各种性能参数，如精确率、召回率和F1分数。
