Yazar "Yildirim, Ozal" seçeneğine göre listele

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    A new super resolution Faster R-CNN model based detection and classification of urine sediments
    (Elsevier, 2023) Avci, Derya; Sert, Eser; Dogantekin, Esin; Yildirim, Ozal; Tadeusiewicz, Ryszard; Plawiak, Pawel
    The diagnosis of urinary tract infections and kidney diseases using urine microscopy images has gained significant attention of medical community in recent years. These images are usually created by physicians' own rule of thumb manually. However, this man-ual urine sediment analysis is usually labor-intensive and time-consuming. In addition, even when physicians carefully examine an image, an erroneous cell recognition may occur due to some optical illusions. In order to achieve cell recognition in low-resolution urine microscopy images with a higher level of accuracy, a new super resolution Faster Region-based Convolutional Neural Network (Faster R-CNN) method is proposed. It aims to increase resolution in low-resolution urine microscopy images using self-similarity based single image super resolution which was used during the pre-processing. De-noising based Wiener filter and Discrete Wavelet Transform (DWT) are used to de-noise high resolution images, respectively, to increase the level of accuracy for image recognition. Finally, for the feature extraction and classification stages, AlexNet, VGFG16 and VGG19 based Faster R-CNN models are used for the recognition and detection of multi-class cells. The model yielded accuracy rates are 98.6%, 96.4% and 96.2% respectively.(c) 2022 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
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    Deep learning model for automated kidney stone detection using coronal CT images
    (Pergamon-Elsevier Science Ltd, 2021) Yildirim, Kadir; Bozdag, Pinar Gundogan; Talo, Muhammed; Yildirim, Ozal; Karabatak, Murat; Acharya, U. Rajendra
    Kidney stones are a common complaint worldwide, causing many people to admit to emergency rooms with severe pain. Various imaging techniques are used for the diagnosis of kidney stone disease. Specialists are needed for the interpretation and full diagnosis of these images. Computer-aided diagnosis systems are the practical approaches that can be used as auxiliary tools to assist the clinicians in their diagnosis. In this study, an automated detection of kidney stone (having stone/not) using coronal computed tomography (CT) images is proposed with deep learning (DL) technique which has recently made significant progress in the field of artificial intelligence. A total of 1799 images were used by taking different cross-sectional CT images for each person. Our developed automated model showed an accuracy of 96.82% using CT images in detecting the kidney stones. We have observed that our model is able to detect accurately the kidney stones of even small size. Our developed DL model yielded superior results with a larger dataset of 433 subjects and is ready for clinical application. This study shows that recently popular DL methods can be employed to address other challenging problems in urology.
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    Deep learning-based PI-RADS score estimation to detect prostate cancer using multiparametric magnetic resonance imaging
    (Pergamon-Elsevier Science Ltd, 2022) Yildirim, Kadir; Yildirim, Muhammed; Eryesil, Hasan; Talo, Muhammed; Yildirim, Ozal; Karabatak, Murat; Acharya, U. Rajendra
    Prostate cancer (PCa) is the most common type of cancer among men. Digital rectal examination and prostate-specific antigen (PSA) tests are used to diagnose the PCa accurately. Since PSA is organ-specific and not disease-specific, multiparametric magnetic resonance imaging (mpMRI) is used to reduce unnecessary biopsies. Prostate imaging reporting and data system (PI-RADS) is widely used for mpMRI scoring to detect PCa. There is low-level agreement among interpreters and also subjectivity associated with PI-RADS scoring. Hence, in this study, a hybrid model has been proposed to accurately interpret mpMRI examination and predict PI-RADS scores. In the proposed systems, feature maps of mpMR images were extracted using the MobilenetV2, Efficientnetb0, and Darknet53 architectures. Then, the feature maps obtained using these three architectures were combined. The merged feature maps are subjected to neighborhood components analysis (NCA) to eliminate redundant features. The proposed system provided 96.09% accuracy.
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    Early detection of colorectal cancer using a hybrid model with enhanced image quality and optimized classification
    (Springer, 2025) Bozdag, Ahmet; Karaduman, Mucahit; Kiziloluk, Soner; Karaduman, Gulsah; Yildirim, Muhammed; Yildirim, Ozal; Acharya, U. Rajendra
    Colorectal cancer starts in the large intestine and rectum. It develops when small, usually harmless growths called polyps become cancerous over time. Early diagnosis increases the chances of successfully treating colorectal cancer. A new hybrid model was developed to detect colorectal tissue types. In the first step of the model, the quality of the images was increased using Denoising Convolutional Neural Network (DNCNN) networks. The feature maps of the images were then obtained using DarkNet53 and shrunk using the Gorilla Troops Optimization Algorithm (GTO) to speed up the proposed model's performance and boost the performance. Finally, a support vector machine (SVM) classifier was used to classify the feature maps. The proposed model obtained an accuracy of 95.5% in classifying eight tissue types in colorectal cancer histopathology specimens (Adipose, Complex, Debris, Empty, Lympho, Mucosa, Stroma, and Tumor). To make the developed model more generalizable, robust, and accurate, it needs to be tested with a huge dataset collected from various centers and races.