Bilgisayar Mühendisliği Bölümü Yayın Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/253

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  • Article
    Citation - WoS: 43
    Citation - Scopus: 50
    Detection of Hip Osteoarthritis by Using Plain Pelvic Radiographs With Deep Learning Methods
    (Springer, 2020) Ureten, Kemal; Arslan, Tayfun; Gultekin, Korcan Emre; Demir, Ayse Nur Demirgoz; Ozer, Hafsa Feyza; Bilgili, Yasemin
    Objective The incidence of osteoarthritis is gradually increasing in public due to aging and increase in obesity. Various imaging methods are used in the diagnosis of hip osteoarthritis, and plain pelvic radiography is the first preferred imaging method in the diagnosis of hip osteoarthritis. In this study, we aimed to develop a computer-aided diagnosis method that will help physicians for the diagnosis of hip osteoarthritis by interpreting plain pelvic radiographs. Materials and methods In this retrospective study, convolutional neural networks were used and transfer learning was applied with the pre-trained VGG-16 network. Our dataset consisted of 221 normal hip radiographs and 213 hip radiographs with osteoarthritis. In this study, the training of the network was performed using a total of 426 hip osteoarthritis images and a total of 442 normal pelvic images obtained by flipping the raw data set. Results Training results were evaluated with performance metrics such as accuracy, sensitivity, specificity, and precision calculated by using the confusion matrix. We achieved accuracy, sensitivity, specificity and precision results at 90.2%, 97.6%, 83.0%, and 84.7% respectively. Conclusion We achieved promising results with this computer-aided diagnosis method that we tried to develop using convolutional neural networks based on transfer learning. This method can help clinicians for the diagnosis of hip osteoarthritis while interpreting plain pelvic radiographs, also provides assistance for a second objective interpretation. It may also reduce the need for advanced imaging methods in the diagnosis of hip osteoarthritis.
  • Article
    Citation - WoS: 41
    Citation - Scopus: 56
    Automated Classification of Rheumatoid Arthritis, Osteoarthritis, and Normal Hand Radiographs With Deep Learning Methods
    (Springer, 2022) Maras, Hadi Hakan; Ureten, Kemal
    Rheumatoid arthritis and hand osteoarthritis are two different arthritis that causes pain, function limitation, and permanent joint damage in the hands. Plain hand radiographs are the most commonly used imaging methods for the diagnosis, differential diagnosis, and monitoring of rheumatoid arthritis and osteoarthritis. In this retrospective study, the You Only Look Once (YOLO) algorithm was used to obtain hand images from original radiographs without data loss, and classification was made by applying transfer learning with a pre-trained VGG-16 network. The data augmentation method was applied during training. The results of the study were evaluated with performance metrics such as accuracy, sensitivity, specificity, and precision calculated from the confusion matrix, and AUC (area under the ROC curve) calculated from ROC (receiver operating characteristic) curve. In the classification of rheumatoid arthritis and normal hand radiographs, 90.7%, 92.6%, 88.7%, 89.3%, and 0.97 accuracy, sensitivity, specificity, precision, and AUC results, respectively, and in the classification of osteoarthritis and normal hand radiographs, 90.8%, 91.4%, 90.2%, 91.4%, and 0.96 accuracy, sensitivity, specificity, precision, and AUC results were obtained, respectively. In the classification of rheumatoid arthritis, osteoarthritis, and normal hand radiographs, an 80.6% accuracy result was obtained. In this study, to develop an end-to-end computerized method, the YOLOv4 algorithm was used for object detection, and a pre-trained VGG-16 network was used for the classification of hand radiographs. This computer-aided diagnosis method can assist clinicians in interpreting hand radiographs, especially in rheumatoid arthritis and osteoarthritis.
  • Article
    Citation - WoS: 2
    Citation - Scopus: 3
    Mining Medline for the Treatment of Osteoporosis
    (Springer, 2012) Ceken, Cinar; Hassanpour, Reza; Esmelioglu, Sadik; Tolun, Mehmet Resit; Yildirim, Pinar
    In this paper, we consider the importance of osteoporosis disease in terms of medical research and pharmaceutical industry and we introduce a knowledge discovery approach regarding the treatment of osteoporosis from a historical perspective. Osteoporosis is a systemic skeletal disease in which osteoporotic fractures are associated with substantial morbidity and mortality and impaired quality of life. Osteoporosis has also higher costs, for example, longer hospital stays than many other diseases such as diabetes and heart attack and it is an attractive market for pharmaceutical companies. We use a freely available biomedical search engine leveraging text-mining technology to extract the drug names used in the treatment of osteoporosis from MEDLINE articles. We conclude that alendronate (Fosamax) and raloxifene (Evista) have the highest number of articles in MEDLINE and seem the dominating drugs for the treatment of osteoporosis in the last decade.
  • Article
    Citation - WoS: 7
    Citation - Scopus: 10
    Prediction of Similarities Among Rheumatic Diseases
    (Springer, 2012) Ceken, Cinar; Hassanpour, Reza; Tolun, Mehmet Resit; Yildirim, Pinar
    We introduce a method for extracting hidden patterns seen in rheumatic diseases by using articles from the widely used biomedical database MEDLINE. Rheumatic diseases affect hundreds of millions of people worldwide and lead to substantial loss of functioning and mobility. Diagnosing rheumatic diseases can be difficult because some symptoms are common to many of them. We use Facta system as a biomedical text mining tool for finding symptoms and then create a dataset with the frequencies of symptoms for each disease and apply hierarchical clustering analysis to find similarities between diseases. Clustering analysis yields four distinct types or groups of rheumatic diseases. Although our results cannot remove all the uncertainty for the diagnosis of rheumatic diseases, we believe they can contribute to the diagnosis of rheumatic diseases to a certain extent. We hope that some similarities exposed can provide additional information at the stage of decision-making.
  • Article
    Citation - WoS: 4
    Citation - Scopus: 7
    Multiple Description Coding for Snr Scalable Video Transmission Over Unreliable Networks
    (Springer, 2014) Choupani, Roya; Wong, Stephan; Tolun, Mehmet
    Streaming multimedia data on best-effort networks such as the Internet requires measures against bandwidth fluctuations and frame loss. Multiple Description Coding (MDC) methods are used to overcome the jitter and delay problems arising from frame losses by making the transmitted data more error resilient. Meanwhile, varying characteristics of receiving devices require adaptation of video data. Data transmission in multiple descriptions provides the feasibility of receiving it partially and hence having a scalable and adaptive video. In this paper, a new method based on integrating MDC and signal-to-noise ratio (SNR) scalable video coding algorithms is proposed. Our method introduces a transform on data to permit transmitting them using independent descriptions. Our results indicate that on average 1.71dB reduction in terms of Y-PSNR occurs if only one description is received.
  • Article
    Citation - WoS: 15
    Citation - Scopus: 21
    New Knowledge in Strategic Management Through Visually Mining Semantic Networks
    (Springer, 2017) Tokdemir, Gul; Sevinc, Mete; Tunc, Murat Mustafa; Ertek, Gurdal
    Today's highly competitive business world requires that managers be able to make fast and accurate strategic decisions, as well as learn to adapt to new strategic challenges. This necessity calls for a deep experience and a dynamic understanding of strategic management. The trait of dynamic understanding is mainly the skill of generating additional knowledge and innovative solutions under the new environmental conditions. Building on the concepts of information processing, this paper aims to support managers in constructing new strategic management knowledge, through representing and mining existing knowledge through graph visualization. To this end, a three-stage framework is proposed and described. The framework can enable managers to develop a deeper understanding of the strategic management domain, and expand on existing knowledge through visual analysis. The model further supports a case study that involves unstructured knowledge of profit patterns and the related strategies to succeed using these patterns. The applicability of the framework is shown in the case study, where the unstructured knowledge in a strategic management book is first represented as a semantic network, and then visually mined for revealing new knowledge.