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Copyright of Karaelmas Science & Engineering Journal / Karaelmas Fen ve Mühendislik Dergisi is the property of Karaelmas Science & Engineering Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Pneumonia is one of the acute lower respiratory tract diseases that can cause severe inflammation of the lung tissue. Although chest X-ray (CXR) is the most common clinical method for diagnosing pneumonia due to its low cost and ease of access, diagnosing pneumonia from CXR images is a difficult task even for specialist radiologists. It has been shown in the literature that deep learning-based image processing is effective in the automatic diagnosis of pneumonia. In conclusion, deep learning-based approaches were used in this study to classify pneumonia and healthy CXR images. These approaches are deep feature extraction, fine-tuning of pre-trained Convolutional Neural Networks (CNN), and end-to-end training of an enhanced ESA model. For deep feature extraction and transfer learning, 10 different pre-trained deep CNN models (AlexNet, ResNet50, DenseNet201, VGG16, VGG19, DarkNet53, ShuffleNet, Squeezenet, NASNetMobile and MobileNetV2) were used. Support Vector Machines (SVM), k Nearest Neighbor (kNN), Random Forest (RF) classifiers are used to classify deep features. The success of the fine-tuned AlexNet model produced an accuracy score of 98.50%, the highest of all results achieved. The end-to-end training of the developed ESA model yielded 96.75% results. The data set used in this study consists of Pneumonia and healthy CXR images obtained from Dicle University Medical Faculty Pulmonary Diseases and Tuberculosis clinic, intensive care unit and pulmonary outpatients' clinic. [ABSTRACT FROM AUTHOR]

The coronavirus, which appeared in Wuhan city of China and named COVID-19 , spread rapidly and caused the death of many people. Early diagnosis is very important to prevent or slow the spread. The first preferred method by clinicians is real-time reverse transcription-polymerase chain reaction (RT-PCR). However, expected accuracy values cannot be obtained in the diagnosis of patients in the incubation period. Therefore, common lung devastation in COVID-19 patients were considered and radiological lung images were used to diagnose. In this study, automatic COVID-19 diagnosis was made from posteroanterior (PA) chest X-Ray images by deep learning method. In the study, using two different deep learning methods, classification was made with different dataset combinations consisting of healthy, COVID, bacterial pneumonia and viral pneumonia X-ray images. The results show that the proposed deep learning-based system can be used in the clinical setting as a supplement to RT-PCR test for early diagnosis

With the coronavirus epidemic (Covid-19) affecting the whole world, urgent but accurate and fast diagnostic methods have been needed for viral diseases such as Covid-19. With the emergence of Covid-19, lung tomography and X-Ray images have been begun to be used by medical doctors to detect Covid-19. It is known that traditional and modern machine learning approaches using X-Ray and tomography images are used for disease diagnosis. In this respect, applications based on artificial intelligence contribute to the sector by showing similar or even better performances to field experts. In this study, for disease diagnosis by using X-Ray lung images, a hybrid support vector machines (SVM) classification model based on the combination of deep and traditional tissue analysis features is proposed. The dataset has been used consists of lung images of healthy, Covid-19, viral pneumonia and lung opacity patients. Hybrid features obtained from X-Ray images have been obtained by using Gray Level Co-occurrence Matrix (GLCM) and DenseNet-201 deep neural network. The performance of hybrid features has been compared to GLCM features as a traditional approach. Both attributes have been trained with SVM. An average of 99.2% accuracy has been achieved in classification success. Other performance measures which have been obtained show that hybrid features are more successful than the traditional method. The proposed artificial intelligence-based method for the diagnosis of Covid-19 has been shown to be promising. [ABSTRACT FROM AUTHOR]

Shoulder implants must be replaced after a certain period of time. But determining the manufacturer or model of the implant during this change is often a mistake-prone and difficult process for medical experts. The aim of this study is to identify 4 different implant manufacturers from 597 shoulder implant X-ray images. For this purpose, both pre-trained CNN architectures (DenseNet201, DenseNet169, InceptionV3, NasNetLarge, VGG16, VGG19 and Resnet50) and cascade models that feed these architectures with YOLOv3 detection algorithm were created and the classification performances of these models were compared. The task of the YOLOv3 detection algorithm in cascade models is to detect the head area of the shoulder implants and give this area as an input to CNN architectures. In addition, traditional machine learning methods were combined with the ensemble learning method and their performance on the data set was revealed. The highest classification performance was achieved in the cascade DenseNet201 model with an accuracy rate of 84.76%. This rate is higher in the literature than in a different study using similar dataset. Classification accuracy of ensemble models is substantially lower than CNN models. Also, the classification accuracy of YOLO supported cascade models is higher than individual CNN models. That is, focusing on the head of the implant with the YOLOV3 detection algorithm has increased the classification accuracy. This method will inspire future studies in this field. [ABSTRACT FROM AUTHOR]

Copyright of SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The COVID-19 is a virus that spreads quickly with a high mortality rate. Rapid and accurate early diagnosis has a key role to reduce the mortality and to decrease the economic cost of this pandemic. For this purpose, diagnostic kits and diagnosis using medical imaging methods have been investigated. Among the medical imaging tools, diagnosis with the help of Computed Tomography and X-ray images is very important. Three different ResNet models (ResNet 50, ResNet 101, and ResNet 152) were investigated (a) to discriminate patients with COVID-19 from normal subjects, (b) to discriminate patients with COVID-19 from patients with Pneumonia, and (c) to discriminate patients with COVID-19, patients with Pneumonia, and normal subjects. ResNet 50 model gave the highest performances among these three models. As a result, we achieved the accuracy of 99.3% to discriminate COVID-19 and Normal, the accuracy of 99.2% to discriminate COVID-19 and Pneumonia, and the accuracy of 97.3% to discriminate COVID-19, Normal, and Pneumonia. In conclusion, the pre-trained ResNet 50 model has a big potential to detect the patients with COVID-19 quickly and accurately using chest X-Ray images only. We believe that this study will help to defeat the epidemic. [ABSTRACT FROM AUTHOR]

Covid-19 has been described as a pandemic by the World Health Organization. It has become an epidemic all over the world and has created a risk for people that may lead to death. To diagnose Covid-19, the diagnosis must be confirmed by RT-PCR test. The test takes a long time and false-negative results can be obtained. If the diagnosis of Covid-19 is made early and correct, the ratio of threats to life is reduced. Deep learning has been widely used in a variety of applications to solve a variety of complex problems that require extremely high accuracy and precision, especially in the medical field. In this study, the Covid-19 is diagnosed automatically using a proposed multi-channel CNN method. Patients and healthy individuals' Lung X-ray images datasets were obtained from three separate online databases. Simple recurrent networks (SRN) architecture was also applied for the same problem to compare the results and demonstrate the efficiency of the proposed method. It is to be noted that to reveal the performance, accuracy and efficiency of the study, accuracy and precision analysis and measurements of processing times for the applied methods were performed. With the proposed system Covid-19 is diagnosed in a short time without waiting for the PCR test and precautions are taken before the virus increases its effect on the body and the risk of individuals' life. Differently from the studies in the literature, the multi-channel CNN architecture with five convolution channels is proposed and the channel selection formulas are presented which are used for selecting the most distinctive feature filters among the results produced by these channels. [ABSTRACT FROM AUTHOR]