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- MakaleAçık Erişim2021Automatic Classification of COVID-19 using CT-Scan Images
Reis, Hatice Catal
Medicine and engineering sciences have been working in close contact for common purposes. Machine learning algorithms are used in the medical field for early diagnosis prediction. The major aim of this study is to evaluate machine learning algorithms and deep learning algorithms using computed tomography scan (CT-scan) images for automated detection of the coronavirus disease 2019 (COVID-19) patients. We obtained seven hundred and fifty-seven (757) CT-scan images from a public platform. We applied four automated traditional classification methods to predict COVID-19 using deep learning and machine learning. These algorithms are SVM, AdaBoost, NASNetMobile, and InceptionV3. Comparative analyses are presented among the four models by considering metric performance factors to find the best model. The results show that the InceptionV3 model achieves better performance in terms of accuracy, precision, recall, Cohen’s kappa, F1-score, root mean squared error (RMSE), and receiver operating characteristic-area under the curve (ROC-AUC), in comparison with the other Covid-19 classifiers. Accordingly, the InceptionV3 approach is recommended for the automatic diagnosis of Covid-19 and assessments. This research can present a second point of view to medical experts and it can save time for researchers as the performance of standard machine learning methods in detecting COVID-19 is evaluated.
- MakaleYalnız künye2021ReCRNet: a deep residual network for crack detection in historical buildings
Reis, Hatice Catal; Khoshelham, Kourosh
In historical buildings, surface cracks are important indicators of potential structural damage. Natural disasters and indirect human factors, which are frequently encountered in recent periods, negatively affect historical buildings and structures. Fast and cost-effective crack detection methods play a key role in structural health monitoring of historical buildings. This paper presents methodologies for identifying concrete cracks using deep learning. We propose ReCRNet, a deep learning architecture designed for classifying images of cracks. The performance of the proposed method is evaluated and compared with state-of-the-art methods such as AlexNet, VGG 19, linear support vector machine (SVM), and decision tree (DT). The results show that ReCRNet achieves better performance in terms of accuracy, precision, recall, F1-score, and receiver operating characteristic (ROC), in comparison with the other crack classifiers. Accordingly, the proposed approach is recommended for automatic monitoring of historical buildings and building condition assessments.
- MakaleYalnız künye2021Cost-oriented LSTM methods for possible expansion of control charting signals
Ünlü, Ramazan
Manual quality control may result in delayed detection of a system defect, or none at all, potentially resulting in malfunctions that can lead to disruption of the system, incur extra costs, or complete downtime of the system. To mitigate such issues, more advanced statistics and machine learning-based systems such as SVM, WSVM, etc. are used to automatically detect error signals during the process. However, these methodologies are not developed for sequential dataset such as a classical CCPR dataset. As a result, in this study, we have implemented a preliminary Cost-Oriented Long-Short Term Memory (LSTM), which is designed to learn from a sequential dataset and compare it with SVM and WSVM which are traditional methods utilized in the field. Additionally, we compared the performance of methods in terms of early detection of an abnormal pattern. Based on the findings, the Cost-Oriented LSTM method outperforms SVM and WSVM in the majority of abnormal patterns in terms of both classification and early detection performance. Global accuracy score which is the average of accuracy rates in all combination of abnormal parameters and window lengths shows that LSTM gives a better accuracy score than SVM and WSVM in all seven abnormal patterns detection.