Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Reis, Hatice Catal(2)
- Akbulut, Zeynep(1)
- Aydin, Tolga(1)
- Bayram, Bulent(1)
- Bingol, Ozkan(1)
- Catal, Hatice(1)
- Corumluoglu, Ozsen(1)
- Coskuncay, Ahmet(1)
- Gedikli, Eyüp(1)
- Günen, Mehmet Akif(1)
- Karsli, Fevzi(1)
- Kavzoglu, Taskin(1)
- Kaya, Serhat(1)
- Khoshelham, Kourosh(1)
- Küçükuğurlu, Büşranur(1)
- Ozdemir, Samed(1)
- Reis, Catal H.(1)
- Seker, Dursun Zafer(1)
- Sığırcı, Ahmet(1)
- Taşolar, Hakan(1)
- Taşolar, Sevgi(1)
- Turk, Salih(1)
- Turk, Veysel(1)
- İlhan Aydın(1)
Yayınlar
- MakaleAçık Erişim2026Scopus SJRQ1A Deep Learning–Based Framework for Automatic Determination of Developmental Dysplasia of the Hip from Graf Angles
Taşolar, Sevgi; Günen, Mehmet Akif; Sığırcı, Ahmet; Taşolar, Hakan
Developmental dysplasia of the hip (DDH) is a common neonatal condition that necessitates early diagnosis to ensure effective treatment. The traditional Graf method, while widely used for evaluating infant hips via ultrasound, is limited by operator dependency and measurement variability. This research has proposed a framework using deep learning network, morphological operation and local maxima method to diagnose DDH in newborns using ultrasound images. The method utilizes DeepLabv3 + for image segmentation, evaluating multiple backbone architectures (ResNet50, InceptionResNetV2, MobilenetV2, and Xception) to identify the region of interest accurately. Local maxima method was used to determine the extremum points of the line defining the Graf angles. Denoising filters, including mean, median, and Wiener, are applied to determine local maxima points accurately. The evaluation comprises two stages: first, assessing the performance of DeepLabv3 + backbones in producing masks for Graf angles determination, and second, comparing the angles obtained through proposed framework with those determined by expert radiologists. Comparative analysis demonstrates that MobileNetV2 (94.64 accuracy, 86.99 Cohen’s kappa, 94.31 F-score) surpasses other models in segmentation accuracy and measurement reliability. This conclusion is backed by key performance metrics such as accuracy, IoU, PSNR, F-score, SSIM, Cohen’s kappa, as well as by the intraclass correlation coefficient and Bland–Altman analyses. The proposed framework shows considerable promise in automating hip ultrasound analysis for DDH diagnosis, minimizing operator dependency while enhancing measurement consistency.
AtıfScopus1WoS2OpenAlex4İncele - MakaleAçık Erişim2024Scopus SJRQ1Extraction of Water Bodies from High-Resolution Aerial and Satellite Images Using Visual Foundation Models
Ozdemir, Samed; Akbulut, Zeynep; Karsli, Fevzi; Kavzoglu, Taskin
Water, indispensable for life and central to ecosystems, human activities, and climate dynamics, requires rapid and accurate monitoring. This is vital for sustaining ecosystems, enhancing human welfare, and effectively managing land, water, and biodiversity on both the local and global level. In the rapidly evolving domain of remote sensing and deep learning, this study focuses on water body extraction and classification through the use of recent deep learning models of visual foundation models (VFMs). Specifically, the Segment Anything Model (SAM) and Contrastive Language-Image Pre-training (CLIP) models have shown promise in semantic segmentation, dataset creation, change detection, and instance segmentation tasks. A novel two-step approach involving segmenting images via the Automatic Mask Generator method of the SAM and the zero-shot classification of segments using CLIP is proposed, and its effectiveness is tested on water body extraction problems. The proposed methodology was applied to both remote sensing imagery acquired from LANDSAT 8 OLI and very high-resolution aerial imagery. Results revealed that the proposed methodology accurately delineated water bodies across complex environmental conditions, achieving a mean intersection over union (IoU) of 94.41% and an F1 score of 96.97% for satellite imagery. Similarly, for the aerial imagery dataset, the proposed methodology achieved a mean IoU of 90.83% and an F1 score exceeding 94.56%. The high accuracy achieved in selecting segments predominantly classified as water highlights the effectiveness of the proposed model in intricate environmental image analysis.
AtıfScopus19WoS13OpenAlex16İncele - MakaleAçık Erişim2024Scopus SJRQ1The impact of implementing backbone architectures on fracture segmentation in X-ray images
Turk, Salih; Bingol, Ozkan; Coskuncay, Ahmet; Aydin, Tolga
X-ray imaging is widely utilized for the detection of bone fractures due to its affordability, rapid processing capabilities, broad accessibility, and ease of use. Despite these advantages, the intricate analysis of X-ray images necessitates advanced computational techniques to fully exploit their rich informational content. Notably, accurate segmentation of these images plays a critical role in aiding medical professionals with precise diagnoses and effective treatment planning. This study examines the impact of integrating different backbone architectures for the task of fracture segmentation in X-ray images. Specifically, the research focuses on enhancing the widely-used DeepLabV3 model by incorporating pre-trained networks such as ResNet50, ResNet101, and MobileNetV3 into the encoder component to improve feature extraction and segmentation accuracy. The FracAtlas dataset, which presents unique challenges due to its small size and the diversity of fractures from various anatomical regions, was employed for model evaluation. Data augmentation techniques were implemented to expand the dataset, and an additional subset focusing on cropped images of fracture areas was developed. The models were trained over 50 epochs, and their performance was assessed using metrics such as Intersection over Union (IoU), loss values, and Dice scores. The results indicate that the DeepLabV3 model with ResNet-based backbones achieved IoU values exceeding 0.93 on the original dataset and demonstrated outstanding performance on the augmented and cropped datasets, with AUC values reaching up to 0.99. The study also highlights the computational complexity of the models, with ResNet101 exhibiting the highest time complexity, while MobileNetV3 was the most efficient in terms of processing time and memory consumption.
AtıfScopus8WoS6OpenAlex6İncele - Yüksek Lisans TeziAçık Erişim2023Evrişimsel sinir ağı kullanılarak göğüs radyografilerinde yüksek başarımlı bölütleme yapılması ve pnömoni tespitine etkisinin incelenmesi
İlhan Aydın
Zatürre diğer adıyla pnömoni bakteriyel veya viral enfeksiyonun neden olduğu yaşamı tehdit eden akciğer hastalığıdır. Bu nedenle, hastalığın erken teşhisi ölüm oranını önemli derecede azaltmaktadır. Göğüs röntgenleri (radyografi) hastalığın teşhisinde büyük öneme sahiptir. Pnömoni teşhisinde sağlık alanındaki çalışmalar dışında, mühendislik alanında da birçok çalışma yapılmakta ve teşhis sürecini hızlandırma konusunda kolaylıklar sağlamaktadır. Bu uygulamalardan biri de derin öğrenme yöntemleri ile hastalığı teşhis etmektir. Çalışmada, Python programlama dilinde Tensorflow Kütüphanesi ve erişime açık veri setlerinden yararlanılmıştır. Veri seti olarak içinde 1-5 yaş arası çocukların göğüs röntgenlerinin bulunduğu Veriseti-1 ve bu veri setinden bir kısım görüntülerin işaretli verilerinin de bulunduğu Veriseti-2 kullanılmıştır. İki aşamalı evrişimsel sinir ağı modeli oluşturulmuştur. İlk aşamada, akciğer bölgesi işaretlenmiş Veriseti-2 üzerinde değiştirilmiş U-Net mimarisi kullanılarak bölütlemesi yapılacak Veriseti-1 görüntüleri için model oluşturulmuştur. Ayrıca çalışmanın son kısmında göğüs kafesi bölgesini tespit edebilecek bir yaklaşım önerilmiştir. Oluşturulan model Veriseti-1'de kullanılarak bölütleme yapılmıştır. Bölütleme doğruluğu (accuracy) % 98.98 olarak hesaplanmıştır. Tasarlanan evrişimsel sinir ağı modeli Veriseti-2'ye uygulanmış ve pnömoni tespitinde test doğruluk (accuracy) değeri %98.55 olarak elde edilmiştir.
- MakaleYalnız künye2022Scopus SJRQ2InSiNet: a deep convolutional approach to skin cancer detection and segmentation
Reis, Hatice Catal; Turk, Veysel; Khoshelham, Kourosh; Kaya, Serhat
Cancer is among the common causes of death around the world. Skin cancer is one of the most lethal types of cancer. Early diagnosis and treatment are vital in skin cancer. In addition to traditional methods, method such as deep learning is frequently used to diagnose and classify the disease. Expert experience plays a major role in diagnosing skin cancer. Therefore, for more reliable results in the diagnosis of skin lesions, deep learning algorithms can help in the correct diagnosis. In this study, we propose InSiNet, a deep learning-based convolutional neural network to detect benign and malignant lesions. The performance of the method is tested on International Skin Imaging Collaboration HAM10000 images (ISIC 2018), ISIC 2019, and ISIC 2020, under the same conditions. The computation time and accuracy comparison analysis was performed between the proposed algorithm and other machine learning techniques (GoogleNet, DenseNet-201, ResNet152V2, EfficientNetB0, RBF-support vector machine, logistic regression, and random forest). The results show that the developed InSiNet architecture outperforms the other methods achieving an accuracy of 94.59%, 91.89%, and 90.54% in ISIC 2018, 2019, and 2020 datasets, respectively. Since the deep learning algorithms eliminate the human factor during diagnosis, they can give reliable results in addition to traditional methods. Graphical abstract: [Figure not available: see fulltext.]
AtıfScopus170WoS313OpenAlex160İncele - MakaleYalnız künye2020Scopus SJRQ1Symbiotic Organisms Search Algorithm for multilevel thresholding of images
Küçükuğurlu, Büşranur; Gedikli, Eyüp
Thresholding is a frequently used method in image processing because of its consistency and low computational cost. Otsu's and Kapur's methods are two important techniques that were proved to be best thresholding methods. However, they have high computational complexity when extended to multilevel thresholding because of their exhaustively search. Recently, meta-heuristic algorithms have been successfully applied for thresholding problems. In this study, six different meta-heuristic algorithms based on Otsu's and Kapur's functions; Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Symbiotic Organisms Search (SOS), Artifical Bee Colony (ABC), Genetic Algorithm (GA) and grey Wolf Optimizer (GWO) were used for multilevel thresholding problem and compared. Experimental results suggest that SOS, PSO and FA algorithms often have higher fitness values than other algorithms. Especially when more than two threshold values are determined, SOS algorithm mostly gives higher fitness values. PSNR and SSIM results of the algorithms are similar. In terms of computational complexity, the GWO algorithm has the fastest convergence. For standard deviations of objective functions; more stable results were obtained with SOS based on Kapur's function, SOS and PSO based on Otsu's function. Also, SOS based on Kapur's function was found to be the most successful algorithm in the Friedman test. As a result, although the GWO approached faster, the SOS algorithm produced more consistent results for both objective functions.
AtıfScopus36WoS24OpenAlex36İncele - MakaleAçık Erişim2018Scopus SJRQ2Detection of foot bone anomaly using medical photogrammetry
Reis, Catal H.
Photogrammetry has been used for medical diagnostic and treatment. Mostly used medical photogrammetric techniques are Ultrasound, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) images. CT and MRI are the most effective method for the early detection of foot and ankle anomaly. Researchers have been developing various methods to detect anomaly. Many image segmentation techniques are available in the literature. Computer Aided Diagnosing (CAD) system has been proposed in this study for detection of foot bone anomaly by the analysis of CT images. In this study, a segmentation based on edge detection method is proposed for the classification of anomaly in foot CT images. Edge detection algorithms are the most commonly used techniques in image processing for edge detection. Canny edge detector is evaluated in this study. In this study, .dicom medical image standard format and ten male patient's foot CT images (245 images and 50 test data) are used. The used parameters are detector collimation of 64 mm, scanning thickness of 1-5 mm, and pixel sizes of 512x512 in radiometric resolution of 16 bits' gray levels. The proposed method consists of five major steps: (i) calculating the horizontal & vertical gradient, (ii) determining gradient magnitude and gradient direction, (iii) applying non-maximal suppression, (iv) computing high and low thresholds, (v) hysteresis thresholding are applied to the multi-detector computed tomography to detect the bone anomaly. In this study, automatic edge-based digital image processing techniques are applied to detect of foot bone anomaly. The proposed canny segmentation method enables users segment anomaly in MDCT of foot very quickly and efficiently. The results demonstrate that the proposed segmentation method is effective for segmenting anomaly. The proposed method obtains satisfactory performances in terms of accuracy and F-measure the area under Receiver Operating Characteristic curve (ROC curve (AUC)). The proposed segmentation method achieves an accuracy of 0.86 and F-measure of 0.92, respectively. The purpose of our study is to detect the anomaly of the foot and it was the simplest and less time consuming process.
AtıfWoS4TR Dizin2İncele - MakaleYalnız künye2016Scopus SJRQ3A semiautomatic segmentation approach to biometric measurement of the talus bone of sedentary women and ballerinas using CT images
Reis, Hatice Catal; Bayram, Bulent; Seker, Dursun Zafer
Background: Ballet produces much stress on bones in the feet of ballerinas. Monitoring and detecting talus bone deformation is important for their quality of health and profession. Objectives: To determine differences in the talus bone between ballerinas and sedentary women. Methods: We evaluated biometric differences in the talus bone of 5 ballerinas referenced to 5 similar sedentary women recruited into the present study. We acquired 20 multidetector computed tomographic images including right and left feet. Semiautomatic region-based image processing using 3D-Doctor (Able Software Corp) was used to create three-dimensional (3D) virtual models of the talus bones. Biometric measurements were made on the 3D models and statistical analysis conducted. Results: The mean talus bone length of ballerinas was 3.37 cm (SD 0.12; range 3.11 to 3.52). The talus bone length of sedentary women was 3.29 cm (SD 0.16; range 3.04 to 3.65), and tended to be shorter than that of the ballerinas (P = 0.08, t test). However, the test was insufficiently powered. The mean volume of the talus bones from the ballerinas was 24.8 cm3 (SD 0.83) and smaller than that from the sedentary women, 26.9 cm3 (SD 1.25) at P < 0.001 (t = 4.38, 18 degrees of freedom. Difference 2.1. Two-tailed 95% confidence interval for difference of means: 1.08 to 3.08). We found less variation in the feet of ballerinas than sedentary women. Conclusions: Volumetric measurements show that feet of ballerinas are smaller and retain similar shape and size than the irregular feet of sedentary women.
AtıfScopus3OpenAlex1İncele - BildiriYalnız künye2012Three-dimensional modeling of the sesamoid bones
Catal, Hatice; Corumluoglu, Ozsen
In this research, digital image processing techniques was used to create 3-dimensional (3D) virtual model of sesamoid bones. As study materials, Ballerina's and sedentary women's axial MDCT (Multidetector Computed Tomography) feet images were utilized. For the determination bone boundaries in each slides, image preprocessing and then segmentation steps were followed. All these processing steps was done in 3DDoctor software. All the measurements were obtained over the 3D model formed. Various statistical ratios were computed regarding the area, volume and length measurements. The comparison done in the study was performed by using these ratios for sedentary and ballerinas. As an outcome, some differences were found in sesamoid bones of ballerina and sedentary women.