Mühendislik Fakültesi / Faculty of Engineering
Permanent URI for this collectionhttps://hdl.handle.net/11727/1401
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Item Classification of Patients with Heart Failure(2014) Bayrak, Tuncay; Ogul, Hasan; https://orcid.org/0000-0001-6826-4350; U-4603-2019Echocardiography is imaging of anatomy and physiology of heart with high frequency sound waves by using ultrasonic transducers. The signals obtained by using this method are defined as echocardiogram. In this way, the function of heart can be investigated and any abnormal case is determined according to many parameters. In this study, the classification was realized, according to 7 of features obtained from echocardiogram signals belong to 74 of patient in Machine Learning Repository (UCI) database. Naive Bayes was determined as the best classification method for this dataset and 63% sensitivity, 84% specificity, and an accuracy value of 77% has been reached. In conclusion, this study presents an investigation of determination of which features are significant in death based on heart failure.Item Author Recognition from Lyrics(2015) Kirmaci, Basar; Ogul, HasanMusic information retrieval has been an important task due to the wide use of internet and related technologies for entertainment. In previous studies, the problem has been considered using the meta-data or melodic content. The use of lyrics in this context is not that common. There is not study either for Turkish songs in this respect. In this study, we discuss the predictability of the author using the text data in a Turkish lyric. To this end, we propose a system that can predict the author using the features extracted from text content. The performance of the system is evaluated on a large data set collected from writers with different music styles.Item Comparison of Similarity Metrics in Microarray Experiment Retrieval(2015) Acici, Koray; Ogul, Hasan; 0000-0002-3821-6419; HDM-9910-2022Content-based retrieval of biological experiments is a recent challenge in bioinformatics. The task is to search in a database using a query-by-example without any meta-data annotation. In this study, for retrieving relevant microRNA experiments from microarray repositories, performance evaluation of known similarity metrics was conducted to compare experiment fingerprints. It was shown that Spearman correlation coefficient outperformed others by comparison on real datasets. This result shows that ranks of fingerprint values are more important than the exact values in experiment fingerprint.Item Context-Sensitive Model Learning for Lung Nodule Detection(2016) Ogul, B. Buket; Ogul, Hasan; Sumer, Emre; AGA-5711-2022Nodule detection in chest radiographs is a main component of current Computer Aided Diagnosis (CAD) systems. The problem is usually approached as a supervised classification task of candidate nodule segments. To this end, a discriminative model is learnt from predefined set of features. A key concern with this approach is the fact that some normal tissues are also imaged and these regions can overlap with the lung tissue as to hide the nodules. These overlaps may reduce the discriminative ability of extracted features and increase the number of false positives accordingly. In this study, we offer to learn distinct models for bone and normal tissue regions following to the segmentation of ribs, which are often the major reason for false positives. Thus, the nodule candidates in bone and normal tissue regions can be assessed in context-sensitive way. The experiments on a common benchmark set determine that the proposed approach can significantly recue the false positives while preserving the sensitivity of detections.Item A Web Application for Content Based Geographic Image Retrieval(2017) Celik, Naime; Ogul, HasanIn this study, a web application is presented for detecting locations with content-based similarity on high resolution orthoimagery via histogram matching methods. For this application, a web interface utilizing Bing Maps for determining query image was constructed via 'Python Flask Micro Web Framework'. This web interface uses the center location of the Bing Maps extent, and the image closest to the center location is used as a query for histogram based matching with other 29106 images in the collection and presenting the user defined number of similar images with an option to convert KMZ files. In order to determine general performance of the application in dataset, 600 of the images were labeled into 6 classes and with 100 images per class. The application was also implemented by using these labeled images and each of them came into use as a query image. The results were calculated as MAP (Mean Average Precision) of each class containing 100 images for each histogram matching method used in this study.Item Monitoring nodule progression in chest X-ray images(2018) Sumer, Emre; Engin, Muharrem; Agildere, Muhtesem; Ogul, HasanLung nodules are frequently observed in cases of cancer. Nodules can be monitored with technologies such as computed tomography (CT) or magnetic resonance imaging (MRI). However. x-ray imaging is a low-cost method as well as its widespread usage. In this context, monitoring the nodules in short intervals by x-ray imaging gives benefits in many aspects. In this study, a three-stage novel approach is proposed to trace the nodule progressions from the lung x-ray images, automatically. In the first stage, x-ray images of a patient taken at different times must be registered to evaluate the nodule progression. To perform the registration, feature extraction and matching methods are employed, and then the homography matrix is calculated. In the second stage, according to previously known nodule positions, matched nodules are detected on registered images. Mismatched nodules in the first image are considered as lost, while the nodules only found in the second image are evaluated as newly appeared. In the last stage, nodules are considered as closed contours consisting of pixel set where closed contour area is calculated after nodule matching process. In this way, growth and shrink states are determined numerically. To test the proposed approach, a patient data set provided by Baskent University, Department of Radiology is used. The validation of the test results is performed by an expert radiologist According to the results obtained, the presented nodule progression trace system is found promising.