Yazar "Uğuz, Harun" seçeneğine göre listele
Listeleniyor 1 - 4 / 4
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe Bone age determination in young children (newborn to 6 years old) using support vector machines(2016) Güraksın, Gür Emre; Uğuz, Harun; Baykan, Ömer KaanBone age is assessed through a radiological analysis of the left-hand wrist and is then compared to chronological age. A conflict between these two values indicates an abnormality in the development process of the skeleton. This study, conducted on children aged between 0 and 6 years, proposes a computer-based diagnostic system to eliminate the disadvantages of the methods used in bone age determination. For this purpose, primarily an image processing procedure was applied to the X-ray images of the left-hand wrist of children from different ethnic groups aged between 0 and 6 years. A total of 9 features, corresponding to the carpal bones and distal epiphysis of the radius bone with some physiological attributes of the children, were obtained. Then, by using gain ratio, the best 6 features were used for the classification process. Next, the bone age determination process was performed with the obtained features with the help of the support vector machine (SVM), naive Bayes, k-nearest neighborhood, and C4.5 algorithms. Finally, the features used in the determination process and their effects on the accuracies were examined. The results of the designed system showed that SVM method has a better achievement rate than the other methods at a rate of 72.82%. Additionally, in this study, a new feature corresponding to the distance between the centers of gravity of the carpal bones was used for the classification process, and the analysis of the related feature showed that there was a statistically significant difference at P <0.05 between this feature and bones in children aged between 0 and 6 years.Öğe Classification of Internal Carotid Artery Doppler Signals Using Hidden Markov Model and Wavelet Transform with Entropy(Springer-Verlag Berlin, 2010) Uğuz, Harun; Kodaz, HalifeDoppler ultrasound has been usually preferred for investigation of the artery conditions in the last two decade, since it is a non-invasive method which is not risky. In this study, a biomedical system based on Discrete Hidden Markov Model (DHMM) has been developed in order to classify the internal carotid artery Doppler signals recorded from 191 subjects (136 of them had suffered from internal carotid artery stenosis and rest of them had been healthy subjects). Developed system comprises of three stages. In the first stage, for feature extraction, obtained Doppler signals were separated to its sub-bands using Discrete Wavelet Transform (DWT). In the second stage, entropy of each sub-band was calculated using Shannon entropy algorithm to reduce the dimensionality of the feature vectors via DWT. In the third stage, the reduced features of carotid artery Doppler signals were used as input patterns of the DHMM classifier. Our proposed method reached 97.38% classification accuracy with 5 fold cross validation (CV) technique. The classification results showed that purposed method is effective for classification of internal carotid artery Doppler signals.Öğe A New Approach Based on Discrete Hidden Markov Model Using Rocchio Algorithm for the Diagnosis of the Brain Diseases(Academic Press Inc Elsevier Science, 2010) Uğuz, Harun; Arslan, AhmetTranscranial Doppler (TCD) study of the adult intracerebral circulation has gained an important popularity in last 10 years, since it is a non-invasive, easy to apply and reliable technique. In this study, an implementation on biomedical system has been developed for classification of signals gathered from middle cerebral arteries in the temporal area via TCD for 24 healthy and 82 ill people which have one of the four different brain patients such as; cerebral aneurysm, brain hemorrhage, cerebral oedema and brain tumor. Basically, the system is composed of feature extraction and classification parts. In the feature extraction stage, the Linear Predictive Coding (LPC) Analysis and Cepstral Analysis were applied in order to extract the cepstral and delta-cepstral coefficients in frame level as feature vectors. In the classification stage a new Discrete Hidden Markov Model (DHMM) based approach was proposed for the diagnosis of brain diseases. This proposed method was developed via Rocchio algorithm. Therefore, to calculate DHMM parameters regulated according to maximum likelihood (ML) approach, both training samples of related class and other classes were included in calculation. Thus, DHMM model parameters presenting one class were suggested to represent the training samples related to that class better as well as not to represent the training samples related to other classes. The performance of the proposed DHMM with Rocchio approach was compared with some methods such as DHMM, Artificial Neural Network (ANN), neuro-fuzzy approaches and obtained better classification performance than these methods.Öğe Saklı Markov model tabanlı sınıflandırıcıların geliştirilemesi(Selçuk Üniversitesi Fen Bilimleri Enstitüsü, 2007) Uğuz, Harun; Arslan, AhmetSınıflandırma; pek çok bilim dalında kullanılan karar verme işlemidir. Sınıflandırma; bir veri gurubu içindeki bir nesneyi temsil eden özelliklerin formüle edildiği ve o nesneyi temsil eden özellikler kullanılarak nesnenin daha önceden belirlenmiş olan sınıflardan birine en düşük hatayla dahil edildiği süreç olarak tanımlanabilir. Saklı Markov Modelleri (SMM) ses ve görüntü tanıma sistemlerinde sıkça kullanılan sınıflandırıcı metotlardan biridir. SMM'lerin esnekliği model topolojisinde ve gözlem dağılımlarında görülür. Bu modeller özellikle ses gibi istatistiksel özellikleri zamanla değişen sinyallerin modellenmesinde kullanılmaktadır. Literatürde genel olarak olasılık yoğunluğu işlevine göre sürekli ve kesikli olmak üzere iki tür SMM sınıflandırıcı yapısından söz edilmektedir. Bu tez çalışması kapsamında hem sürekli hemde kesikli SMM sınıflandırıcıları üzerinde durularak mevcut SMM sınıflandırıcı yöntemlerinin tespit edilen eksik yanları giderilerek SMM`de kullanılan algoritmaların performanslarının artırılması amaçlanmıştır. Bu amaçla bulanık mantık, genetik algoritmalar, bulanık integraller, kümeleme algoritmaları gibi bir dizi teknikten faydalanılmıştır. Geliştirilen SMM tabanlı yeni sınıflandırıcı yaklaşımlarının sınıflandırma başarıları Fırat Tıp Merkezi kardiyoloji kliniğinde hastalıklı ve sağlıklı kişilerden elde edilen Doppler kalp verileri ile Transcranial Doppler yöntemi ile elde edilen beyin hastalıklarına ait Doppler verileri üzerinde test edilmiştir. Elde edilen sınıflandırma sonuçları kullanılan veri kümeleri üzerinde yapılmış olan daha önceki çalışmalara ait sınıflandırma sonuçları ile kıyaslanarak gerçekleştirilen yeni sınıflandırıcı yaklaşımların başarıları ortaya konulmuştur.
