Yazar "Ucuzal, Hasan" seçeneğine göre listele

Listeleniyor 1 - 7 / 7
  • Küçük Resim Yok
    Öğe
    Automated Classification of Brain Tumors by Deep Learning-Based Models on Magnetic Resonance Images Using a Developed Web-Based Interface
    (Duzce Univ, Fac Medicine, 2021) Tetik, Bora; Ucuzal, Hasan; Yasar, Seyma; Colak, Cemil
    Objective: Primary central nervous system tumors (PCNSTs) compose nearly 3% of newly diagnosed cancers worldwide and are more common in men. The incidence of brain tumors and PCNSTs-related deaths are gradually increasing all over the world. Recently, many studies have focused on automated machine learning (AutoML) algorithms which are developed using deep learning algorithms on medical imaging applications. The main purposes of this study are -to demonstrate the use of artificial intelligence-based techniques to predict medical images of different brain tumors (glioma, meningioma, pituitary adenoma) to provide techicalsupport to radiologists and -to develop a user-friendly and free web-based software to classify brain tumors for making quick and accurate clinical decisions. Methods: Open-sourced T1-weighted magnetic resonance brain tumor images were achieved from Nanfang Hospital, Guangzhou, China, and General Hospital, Tianjin Medical University, To construct the proposed system which web-based interface and the deep learning-based models, the Keras/Auto-Keras library, which is employed in Python's programming language, is used. Accuracy, sensitivity, specificity, G-mean, F-score, and Matthews correlation coefficient metrics were used for performance evaluations. Results: While 80% (2599 instances) of the dataset was used in the training phase, 20% (465 instances) was employed in the testing phase. All the performance metrics were higher than 98% for the classification of brain tumors on the training data set. Similarly, all the evaluation metrics were higher than 91% except for sensitivity and MCC for meningioma on the testing dataset. Conclusions: The results from the experiment reveal that the proposed software can be used to detect and diagnose three types of brain tumors. This developed web-based software can be accessed freely in both English and Turkish at http://biostatapps.inonu.edu.tr/BTSY/.
  • Küçük Resim Yok
    Öğe
    A Computer-Assisted Diagnosis Tool for Classifying COVID-19 based on Chest X-Ray Images
    (Duzce Univ, Fac Medicine, 2021) Yagin, Fatma Hilal; Guldogan, Emek; Ucuzal, Hasan; Colak, Cemil
    Objective: Since COVID-19 is a worldwide pandemic, COVID-19 detection using a convolutional neural network (CNN) has been an extraordinary research technique. In the reported studies, many models that can predict COVID-19 based on deep learning methods using various medical images have been created; however, clinical decision support systems have been limited. The aim of this study is to develop a successful deep learning model based on X-ray images and a computer-assisted, fast, free and web-based diagnostic tool for accurate detection of COVID-19. Methods: In this study a 15-layer CNN model was used to detect COVID-19 using X-ray images, which outperformed many previously published CNN models in terms of classification. The model performance is evaluated according to Accuracy, Matthews Correlation Coefficient (MCC), F1 Score, Specificity, Sensitivity (Recall), Youden's Index, Precision (Positive Predictive Value: PPV), Negative Predictive Value (NPV), and Confusion Matrix (Classification matrix). In the second phase of the study, the computer-aided diagnostic tool for COVID-19 disease was developed using Python Flask library, JavaScript and Html codes. Results: The model to diagnose COVID-19 has an average accuracy of 98.68 % in the training set and 96.98 % in the testing set. Among the evaluation metrics, the minimum value is 93.4 % for MCC and Youden's index, and the maximum value is 97.8 for sensitivity and NPV. A higher sensitivity value means a lower false negative (FN) value, and a low FN value is an encouraging outcome for COVID-19 cases. This conclusion is crucial because minimizing the overlooked cases of COVID-19 (false negatives) is one of the main goals of this research. Conclusions: In this period when COVID-19 is spreading rapidly around the world, it is thought that the free and web-based COVID-19 X-Ray clinical decision support tool can be a very effective and fast diagnostic tool. The computer-aided system can assist physicians and radiologists in making clinical decisions about the disease, as well as provide support in diagnosis, follow-up, and prognosis. The developed computer-assisted diagnosis tool can be publicly accessed at http://biostatapps.inonu.edu.tr/CSYX/..
  • Küçük Resim Yok
    Öğe
    Deep learning based-classification of dementia in magnetic resonance imaging scans
    (Ieee, 2019) Ucuzal, Hasan; Arslan, Ahmet K.; Colak, Cemil
    Deep learning is much preferred in image processing applications since it can give fast and important results. This research aims at developing an open source software for deep learning based-classification of dementia in magnetic resonance imaging scans. Keras (i.e., a deep-learning framework) is employed for constructing a deep learning based-model that could be discriminate between dementia patients and healthy individuals. The achieved findings demonstrate that the proposed system can be used to detect individuals with suspected dementia disease.
  • Küçük Resim
    Öğe
    Detection of Different Tissue Types of Colorectal Cancer Based on Histological Images Using Deep Learning Approach
    (2021) Güldoğan, Emek; Küçükakçalı, Zeynep; Ucuzal, Hasan; Çolak, Cemil
    Objective: Automatic machine learning methods developed by employing deep learning approaches have been the focus of numerous studies nowadays. The objective of the current study is to design a web-based software that is used in the classification of tissue samples in colorectal cancer, based on eight different histopathological tissue types, to support physicians for the clinical diagnosis of colorectal cancer, and thus to enable physicians to make quick and accurate decisions. Material and Methods: An open-access data set (DOI: 10.5281/zenodo.53169) consisting of 5,000 histopathological images, including different histopathological tissue types of colorectal cancer, was used in the present study. Keras-based AutoKeras library was applied to classify the histopathological tissue types of colorectal cancer. Appropriate python language libraries were employed in the development of the web-based software. A deep learning-based model was constructed to predict eight histopathological tissue types of colorectal cancer. Results: The highest metric values among the performance criteria achieved for different tissue types of colorectal cancer were calculated for adipose type, and we found that accuracy was 0.996, sensitivity 0.992, specificity 0.996, precision 0.974, recall 0.992, and F1-score 0.983, respectively. This research differs from other studies in that it includes open access software. Conclusion: The web software based on the model proposed in this study provided promising predictions in classifying different tissue types from histopathological images of colorectal cancer. Thanks to the proposed software, the tissue types of colorectal cancer are easily understood by medical professionals and other healthcare workers. Hence, the workload of medical professionals can be reduced, and a faster consultation system can be formed.
  • Küçük Resim Yok
    Öğe
    Development of Artificial Intelligence Based Clinical Decision Support System on Medical Images for the Classification of COVID-19
    (2023) Çolak, Cemil; Arslan, Ahmet Kadir; Ucuzal, Hasan; Köse, Adem; Yıldırım, İsmail Okan; Güldoğan, Emek; Çolak, M. Cengiz
    Aim: The first imaging method to play an vital role in the diagnosis of COVID-19 illness is the chest X-ray. Because of the abundance of large-scale annotated picture datasets, convolutional neural networks (CNNs) have shown considerable performance in image recognition/classification. The current study aims to construct a successful deep learning model that can distinguish COVID-19 from healthy controls using chest X-ray images.Material and Methods: The dataset in the study consists of subjects with 912 negative and 912 positive PCR results. A prediction model was built using VGG-16 with transfer learning for classifying COVID-19 chest X-ray images. The data set was split at random into 80% training and 20% testing groups.Results: The accuracy, F1 score, sensitivity, specificity, positive and negative values from the model that can successfully distinguish COVID-19 from healthy controls are 97.3%, 97.3%, 97.8%, 96.7%, 96.7%, and 97.8% regarding the testing dataset, respectively.Conclusion: The suggested technique might greatly improve on current radiology-based methodologies and serve as a beneficial tool for clinicians/radiologists in diagnosing and following up on COVID-19 patients.
  • Küçük Resim Yok
    Öğe
    Investigation of Usability of Artificial Intelligence Semantic Video Processing Methods in Medicine
    (2022) Ucuzal, Hasan; Tunç, Zeynep; Güldoğan, Emek
    Aim: The goal of this study is to produce user-friendly software for healthcare professionals with various approaches such as detection, identification, classification, and tracking of polyps contained in endoscopic images utilizing appropriate video/image processing techniques and CNN architecture. Material and Method: There were 345 photos in total in the study. These photographs are images depicting anatomical milestones, clinical findings, or gastrointestinal procedures in the digestive tract that have been documented and validated by medical specialists (skilled endoscopists). Each class has hundreds of images. The photos were downloaded from https://datasets.simula.no/kvasir, which is a free source for educational and research purposes. In the modeling phase, CNN and the Max-Margin object detection technique (MMOD), one of the deep neural network designs in the Dlib package, were employed. The data set was separated as 80% training and 20% test dataset using the simple cross-validation method (hold-out). Precision, recall, F1-score, average precision (AP), mean average precision (mAP), ideal localization recall precision (oLRP), mean optimal LRP (moLRP), and intersection over union (IoU) were used to evaluate model performance. Results: When the previously described steps were performed on the open-access video image dataset of endoscopic polyps in the current study, all performance metrics examined in the training dataset received a value of 1, whereas, in the test dataset precision, sensitivity, F1-score, AP, mAP, oLRP, and moLRP were 98%, 90%, 94%, 89%, 89%, 48%, and 48% respectively. Conclusion: The proposed approach was found to make accurate predictions in the diagnosis of gastrointestinal polyps based on the values of the calculated performance criteria.
  • Küçük Resim
    Öğe
    Yapay zekâya dayalı anlamsal video işleme yöntemlerinin tıpta kullanılabilirliğinin araştırılması
    (İnönü Üniversitesi, 2020) Ucuzal, Hasan
    Amaç: Nesne algılama veya tanıma, dijital görüntülerdeki nesneleri tanımlamayı içeren ilgili bilgisayarlı görü işlemlerinin bir koleksiyonunu tanımlayan genel bir terimdir. Son zamanlarda sağlık alanında nesne algılama uygulamalarının klinik karar destek sistemleri olarak kullanımına yönelik çalışmalar giderek önem kazanmaktadır. Gelişen bilgisayar ve makine öğrenmesi teknolojileri sayesinde klinik görüntü ve videolar (Bilgisayarlı tomografi, ultrason, vb.) üzerinden hastalık veya anomali tespiti bilgisayarlar tarafından otomatik olarak yapılabilir hale gelmiştir. Hesaplama maliyeti yüksek bu işlemlerin yüksek doğruluk ve hassasiyetle gerçekleştirilebilmesi için amaca yönelik derin öğrenme mimarileri geliştirilmiştir. Nesne tanıma işlemleri için sıklıkla kullanılan mimarilerden olan evrişimsel sinir ağları (Convolutional neural networks, CNN) derin, ileri beslemeli yapay sinir ağı sınıfıdır. Bu çalışmada uygun video/görüntü işleme teknikleri ve CNN mimarisi kullanılarak endoskopik video görüntüleri içerisinde bulunan poliplerin tespiti, tanımlanması, sınıflandırılması ve takibi gibi çeşitli yöntemler ile sağlık personelleri için kullanıcı dostu bir yazılımın geliştirilmesi amaçlanmıştır. Materyal ve Metot: Çalışmada kullanılan veriseti, toplam 300 görüntüden oluşmaktadır. Bu görüntüler sindirim yolundaki anatomik dönüm noktaları, patolojik bulgular veya gastrointestinal prosedürler gibi her sınıf için yüzlerce görüntüden oluşan çeşitli sınıflara ait tıp doktorları (deneyimli endoskopistler) tarafından açıklanan ve doğrulanan görüntülerden oluşmaktadır. Görüntüler, araştırma ve eğitim amaçlı olarak açık erişimli olarak sunulan https://datasets.simula.no/kvasir internet adresinden temin edilmiştir. Modelleme aşamasında Dlib kütüphanesinde bulunan derin sinir ağları mimarilerinden CNN ve maksimum aralık nesne algılama yöntemi (Max-Margin Object Detection, MMOD) kullanılmıştır. Basit çapraz geçerlilik yöntemi (hold-out) kullanılarak veri seti %80 eğitim, %20 test olacak şekilde iki kısma ayrılmıştır. Model performansının değerlendirilmesinde ise kesinlik, duyarlılık, F1-skor, ortalama kesinlik (average precision, AP), ortalama kesinlik değerlerinin ortalaması (mean average precision, mAP), kesiştirilmiş bölgeler ölçütleri (intersection over union, IoU), en uygun konumlandırma kesinliği ve duyarlılığı (optimal localization recall precision, oLRP), ortalama en uygun LRP (Mean Optimal LRP, moLRP) kullanılmıştır. Bulgular: Bu tez çalışmasının uygulamasında endoskopik poliplere ilişkin açık erişimli video görüntü veri seti üzerinde önceden açıklanan aşamalar gerçekleştirildiğinde, eğitim veri setinde incelenen bütün performans metrikleri 1 değerini alırken, test veri setinde ise kesinlik %98, duyarlılık %90, F1-skoru %94, AP ve mAP %89, oLRP ve moLRP %48 olarak hesaplanmıştır. Sonuç: Hesaplanan performans ölçütlerine ait değerler dikkate alındığında, önerilen sistemin gastrointestinal poliplerin tanısında başarılı tahminler verdiği tespit edilmiştir. Anahtar kelimeler: Nesne tanıma, derin öğrenme, karar destek sistemi, gastrointestinal polipler, evrişimsel sinir ağları