Yazar "Cengil, Emine" seçeneğine göre listele
Listeleniyor 1 - 5 / 5
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model(Mdpi, 2023) Yildirim, Muhammed; Bingol, Harun; Cengil, Emine; Aslan, Serpil; Baykara, MuhammetUrine sediment examination is one of the main tests used in the diagnosis of many diseases. Thanks to this test, many diseases can be detected in advance. Examining the results of this test is an intensive and time-consuming process. Therefore, it is very important to automatically interpret the urine sediment test results using computer-aided systems. In this study, a data set consisting of eight classes was used. The data set used in the study consists of 8509 particle images obtained by examining the particles in the urine sediment. A hybrid model based on textural and Convolutional Neural Networks (CNN) was developed to classify the images in the related data set. The features obtained using textural-based methods and the features obtained from CNN-based architectures were combined after optimizing using the Minimum Redundancy Maximum Relevance (mRMR) method. In this way, we aimed to extract different features of the same image. This increased the performance of the proposed model. The CNN-based ResNet50 architecture and textural-based Local Binary Pattern (LBP) method were used for feature extraction. Finally, the optimized and combined feature map was classified at different machine learning classifiers. In order to compare the performance of the model proposed in the study, results were also obtained from different CNN architectures. A high accuracy value of 96.0% was obtained in the proposed model.Öğe Classification of the weather images with the proposed hybrid model using deep learning, SVM classifier, and mRMR feature selection methods(2022) Yildirim, Muhammed; Çinar, Ahmet; Cengil, EmineAs in many fields, the use of artificial intelligence methods in the classification of weather images will be very useful. In this study, a data set consisting of five classes such as cloudy, foggy, rainy, shine, and sunrise was used. A hybrid model has been developed to classify the images in the dataset. First of all, the features of the images in the dataset are obtained by using MobilenetV2, Densenet201, and Efficientnetb0 architectures, which are the most popular Convolutional Neural Network (CNN) architectures. These features are combined and optimized so that these optimized features are classified in the Support Vector Machine (SVM) classifier, one of the most popular classifier methods in machine learning. As a result, the developed hybrid model has outperformed the existing pre-trained architectures in the study. In addition, it has been proven that classification by concatenating the features obtained with CNN architectures is a successful method.Öğe COVID-19 Detection on Chest X-ray Images with the Proposed Model Using Artificial Intelligence and Classifiers(Springer, 2022) Yıldırım, Muhammed; Eroğlu, Orkun; Eroğlu,Yeşim; Çınar, Ahmet; Cengil, EmineCoronavirus disease-2019 (COVID-19) is a serious infectious disease that is spreading rapidly all over the world. Scientists are looking for alternative diagnostic methods to detect and control the disease early. Artifcial intelligence applications are promising in the COVID-19 epidemic. This paper proposes a hybrid approach for diagnosing COVID-19 on chest X-ray images and diferentiation from other viral pneumonia. The model we propose consists of three steps. In the frst step, classifcation was made using the MobilenetV2, Efcientnetb0, and Darknet53 deep models. In the second step, the feature maps of the images in the Chest X-ray data set were extracted separately for each architecture using the MobilenetV2, Efcientnetb0, and Darknet53 architectures. NCA method was preferred to reduce the size of these feature maps obtained. The feature maps obtained after dimension reduction were classifed in the classic machine learning classifers. In the third step, the feature maps obtained from each architecture were combined. After dimension reduction was applied to these combined features by applying the NCA method, this feature map is classifed in the classifers. The model we proposed was tested on two diferent data sets. The accuracy values obtained in these data sets are 99.05 and 97.1%, respectively. The obtained accuracy values show that the model is successful.Öğe Detection and classification of glioma, meningioma, pituitary tumor, and normal in brain magnetic resonance imaging using deep learning-based hybrid model(Springer International Publishing, 2023) Yildirim, Muhammed; Cengil, Emine; Eroglu, Yeşim; Cinar, AhmetPituitary, meningioma, and glioma tumors are the primary widespread brain tumors. Treatment algorithms for these tumors may differ from each other. For this reason, the detection and typing of brain tumors are necessary to determine the appropriate treatment quickly. Since imaging findings vary, even experts often have difficulties in classifying brain tumors. In this study, a convolutional neural network-based hybrid model is developed to classify glioma, meningioma, pituitary tumors, and normal brain magnetic resonance images. In the proposed hybrid model, features are obtained using pre-trained Efficientnetb0 and Shufflenet architectures. Also, the images in the existing dataset are improved, such as colored and used as the second database. With the proposed two pre-trained models, the features of individual images from two datasets are extracted. Later, these features are concatenated, and the best of these features are selected using the mRMR feature reduction method and classified using the Support Vector Machine (SVM) classifier. The proposed method achieved better results than the previously trained Efficientnetb0 and Shufflenet architectures. The accuracy value of the proposed hybrid model is 95.4%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.Öğe Erken Diyabet Tanısında Makine Öğrenme Modellerinin Rolü: Veri Seti Tabanlı Bir Analiz(Ali KARCI, 2025) Çengel, Ekemen; Cengil, Emine; Yıldırım, MuhammedDiyabet, kandaki glikoz düzeyinin normalin üzerine çıktığı kronik bir metabolik hastalıktır. Bunun temel nedeni pankreasın yeterli insülin üretememesi veya üretilen insülinin etkili bir şekilde kullanılamamasıdır. Diyabetin yönetilebilmesi ve komplikasyonların önlenebilmesi için erken tanı olmazsa olmazdır. Makine öğrenimi gibi ileri teknolojiler erken tanıda yüksek doğruluk oranları sağlayarak hem bireysel sağlık yönetimine hem de toplum sağlığı sistemlerine katkı sağlamaktadır. Bu çalışmada, diyabetin erken tanısında makine öğrenimi yöntemlerinin rolünün incelenmesi amaçlanmıştır. Bu amaçla yöntemler iki farklı veri seti üzerinde analiz edilmiştir. Destek Vektör Makineleri, Karar Ağaçları ve Yapay Sinir Ağları kullanılan makine öğrenimi sınıflandırıcıları arasındaydı. Her iki veri setinde de modellerin doğruluk, duyarlılık ve özgüllük gibi metrikler açısından performansları değerlendirilmiş ve karşılaştırılmıştır. Sonuçlara göre Bagged Trees algoritması kullandığımız ilk veri seti olan BIT Mesra Veri kümesinde %96,2 ile en iyi performansı göstermiştir. Pima Indian veri kümesinde, SVM algoritması %77,2'lik bir doğruluk oranına ulaştı. Çalışma, diyabetin erken teşhisi için bir yöntem sunmakta ve bu alanda veri çeşitliliğinin önemini vurgulamaktadır.
