Browsing by Author "Akyol, K, Sen, B"

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Automatic Detection of Covid-19 with Bidirectional LSTM Network Using Deep Features Extracted from Chest X-ray Images.
(2022-03-01T00:00:00Z) Akyol, Kemal, Şen, Baha; Akyol, K, Sen, B
Coronavirus disease, which comes up in China at the end of 2019 and showed different symptoms in people infected, affected millions of people. Computer-aided expert systems are needed due to the inadequacy of the reverse transcription-polymerase chain reaction kit, which is widely used in the diagnosis of this disease. Undoubtedly, expert systems that provide effective solutions to many problems will be very useful in the detection of Covid-19 disease, especially when unskilled personnel and financial deficiencies in underdeveloped countries are taken into consideration. In the literature, there are numerous machine learning approaches built with different classifiers in the detection of this disease. This paper proposes an approach based on deep learning which detects Covid-19 and no-finding cases using chest X-ray images. Here, the classification performance of the Bi-LSTM network on the deep features was compared with the Deep Neural Network within the frame of the fivefold cross-validation technique. Accuracy, sensitivity, specificity and precision metrics were used to evaluate the classification performance of the trained models. Bi-LSTM network presented better performance compare to DNN with 97.6% value of high accuracy despite the few numbers of Covid-19 images in the dataset. In addition, it is understood that concatenated deep features more meaningful than deep features obtained with pre-trained networks by one by, as well. Consequently, it is thought that the proposed study based on the Bi-LSTM network and concatenated deep features will be noteworthy in the design of highly sensitive automated Covid-19 monitoring systems.
HANDLING the EFFECT of ATTRIBUTE SELECTION on SUPPORT VECTOR MACHINES for DETECTING CHRONIC KIDNEY DISEASE
(2022-12-01) Akyol K., Şen B.; Akyol, K, Sen, B
Chronic kidney disease is a gradual loss of kidney function. Determining the important attributes that describe this disease plays a key role in screening and examining the disease by field specialists. The main aim of this study is to comprehensively compare the attribute selection algorithms for predicting this disease. With this aim, several models were built and compared using well-known performance metrics such as accuracy, sensitivity, and specificity in the experiments. Two different attribute selection methods; the stability selection and the minimum redundancy maximum relevance were compared comprehensively on the unbalanced and balanced datasets. In this framework, the stability selection method gave the important attributes. The support vector machines with radial bases function kernel successfully performed the classification using these attributes for this problem.
Keypoint detectors and texture analysis based comprehensive comparison in different color spaces for automatic detection of the optic disc in retinal fundus images
(2021-09-01) Akyol K., Şen B.; Akyol, K, Sen, B
Detection of the optic disc which has similar brightness with the hard and soft exudate lesions seen in the early stage of diabetic retinopathy is very difficult due to different light conditions and contrast values. Automatic detection of these lesions by expert systems in the medical field is very important. In this context, we propose a new approach based on the analysis of color spaces, keypoint detectors, and texture for retinal fundus images. If the keypoint information is contained within the actual optic disc region, this is an important consideration for the automated detection of the optic disc. This study can be divided into five sections, respectively, image preprocessing, image processing, keypoint detection, texture analysis, and performance evaluation. The analyses of patch images compatible with the keypoints obtained from the Red–Green–Blue (RGB) image and its color channels were carried out. The performance of the study was validated on the Digital Retinal Images for Vessel Extraction public dataset. According to the results, Local Binary Pattern texture analysis performed in region of interest around keypoints detected by different keypoint detectors presented good performance in RGB and green channel images.
Modeling and predicting of news popularity in social media sources
(2019-01-01) Akyol K., Şen B.; Akyol, K, Sen, B
The popularity of news, which conveys newsworthy events which occur during day to people, is substantially important for the spectator or audience. People interact with news website and share news links or their opinions. This study uses supervised learning based machine learning techniques in order to predict news popularity in social media sources. These techniques consist of basically two phrases: a) the training data is sent as input to the classifier algorithm, b) the performance of prelearned algorithm is tested on the testing data. And so, a knowledge discovery from the data is performed. In this context, firstly, twelve datasets from a set of data are obtained within the frame of four categories: Economic, Microsoft, Obama and Palestine. Second, news popularity prediction in social network services is carried out by utilizing Gradient Boosted Trees, Multi-Layer Perceptron and Random Forest learning algorithms. The prediction performances of all algorithms are examined by considering Mean Absolute Error, Root Mean Squared Error and the R-squared evaluation metrics. The results show that most of the models designed by using these algorithms are proved to be applicable for this subject. Consequently, a comprehensive study for the news prediction is presented, using different techniques, drawing conclusions about the performances of algorithms in this study.