Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleYalnız künye2026Efficient ensemble learning with multi-scale fusion based deep neural network for skin lesion classification
Reis, Hatice Catal; Turk, Veysel
Melanoma, a type of skin cancer, is a severe skin cancer that is critical for early diagnosis but difficult to detect. Deep learning is promising in melanoma diagnosis because it can automatically analyze visual features and perform end-to-end learning. However, achieving the optimum balance between performance and computational efficiency for the effective use of these models in clinical applications is an important research topic. In this study, three innovative methods are proposed for skin cancer detection: PADSCNet, P-DEFF, and M–DEFF. The PADSCNet is an effective neural network developed with CNN and multi-head attention techniques, designed with fusion methods, and stands out with its minimum number of parameters despite being quite deep. The P-DEFF and M–DEFF are based on ensemble learning techniques; P-DEFF was developed with a single deep learning model, and M–DEFF was developed with multiple pre-trained deep learning models. The proposed approaches were tested on the Skin Cancer: Malignant vs. Benign dataset, which was pre-processed using image enhancement techniques. According to the experimental results, PADSCNet achieved accuracy rates of 88.12 %, P-DEFF 93.75 %, and M–DEFF 95.00 %. In additional experiments conducted with the PADSCNet model, 93.12 % accuracy rates were achieved in the CNN for Melanoma Detection Dataset, and 95.00 % accuracy rates were achieved in the PH2 dataset. These findings show that the proposed methods are effective in skin cancer detection and can achieve successful results in clinical applications.
- MakaleYalnız künye2026Multi-scale deep fusion architecture for brain tumor detection via local-contextual feature integration
Reis, Hatice Catal; Turk, Veysel
Early detection of brain tumors is crucial for enhancing treatment strategies and improving clinical prognosis. However, current conventional methods limit diagnostic accuracy and slow down diagnostic processes due to subjective interpretations and dependence on experts. Deep learning-based approaches have the potential to enhance diagnostic accuracy and facilitate early diagnosis by mitigating current limitations in automatic learning techniques for feature extraction and modeling processes. In this study, an advanced deep learning model, Multi-Scale Local-Context Fusion Network (MSLCFNet), is proposed for automatic diagnosis of brain tumors within the scope of oncological diseases. The proposed model evaluates local spatial features, global patterns, and long-range contextual relationships holistically through multi-layered representations, enabling effective identification of complex structures of tumor morphology. This architecture has the potential to increase diagnostic consistency and minimize the risk of misdiagnosis by reducing inter-observer variability, especially in cases where limited contrast and unclear boundaries lead to diagnostic uncertainty. The proposed model achieved accuracy rates of 96.51% and 98.17% on two different MRI brain tumor datasets, respectively. Additionally, a hybrid model based on MSLCFNet+ResNet50 was proposed in the study to obtain high-level feature representations for brain tumor diagnosis. The model offers a significant improvement in the diagnosis process by reducing misdiagnosis rates to 98.86% and 99.50% accuracy. The proposed methods make a significant contribution to enhancing diagnostic accuracy in clinical decision support systems, as they enable the efficient processing of complex morphological structures.
- MakaleYalnız künye2026A multi-scale context-aware deep learning framework for medical disease diagnosis
Reis, Hatice Catal; Turk, Veysel
Early and accurate diagnosis of brain tumors, skin lesions, and diabetic retinopathy is essential for effective treatment planning and improving patient prognosis. Nevertheless, the diagnostic process for these conditions remains challenging due to the reliance on conventional methods, which are often subjective, time-intensive, and dependent on expert interpretation. In this context, deep learning-based approaches offer promising opportunities to enhance diagnostic precision and support clinical decision-making. This study introduces BSCRADNet, a novel deep learning model designed for the automated diagnosis of oncological and retinopathic diseases. The proposed architecture employs a multi-layered analytical framework that integrates local and spatial features with long-range contextual dependencies, enabling the effective recognition of complex morphological patterns in medical images. By enhancing diagnostic consistency, BSCRADNet reduces the risk of misclassification—particularly in cases characterized by high inter-observer variability or subtle lesion presentation. The model demonstrated strong performance across multiple datasets, achieving classification accuracies of 94.67% for brain tumors, 89.58% for skin cancer, and 90.40% for diabetic retinopathy. Furthermore, to improve the discriminative power of feature representations across diverse disease categories, a composite hybrid architecture combining BSCRADNet with ResMLP was developed. This hybrid model yielded competitive results, with accuracies of 93.33%, 88.19%, and 87.40% for the respective diagnostic tasks. Experimental findings indicate that the proposed approach is capable of distinguishing intricate patterns in medical imaging data and holds significant potential for integration into clinical diagnostic workflows, contributing to more reliable and efficient disease classification.
- MakaleYalnız künye2025A deep neural network combined with a two-stage ensemble model for detecting cracks in concrete structures
Reis, Hatice Catal; Turk, Veysel; Kaya Yildiz, Cagla Melisa; Bozkurt, Muhammet Furkan; Karagoz, Seray Nur; Ustuner, Mustafa
Detection of cracks in concrete structures is critical for their safety and the sustainability of maintenance processes. Traditional inspection techniques are costly, time-consuming, and inefficient regarding human resources. Deep learning architectures have become more widespread in recent years by accelerating these processes and increasing their efficiency. Deep learning models (DLMs) stand out as an effective solution in crack detection due to their features such as end-to-end learning capability, model adaptation, and automatic learning processes. However, providing an optimal balance between model performance and computational efficiency of DLMs is a vital research topic. In this article, three different methods are proposed for detecting cracks in concrete structures. In the first method, a Separable Convolutional with Attention and Multi-layer Enhanced Fusion Network (SCAMEFNet) deep neural network, which has a deep architecture and can provide a balance between the depth of DLMs and model parameters, has been developed. This model was designed using a convolutional neural network, multi-head attention, and various fusion techniques. The second method proposes a modified vision transformer (ViT) model. A two-stage ensemble learning model, deep feature-based two-stage ensemble model (DFTSEM), is proposed in the third method. In this method, deep features and machine learning methods are used. The proposed approaches are evaluated using the Concrete Cracks Image Data set, which the authors collected and contains concrete cracks on building surfaces. The results show that the SCAMEFNet model achieved an accuracy rate of 98.83%, the ViT model 97.33%, and the DFTSEM model 99.00%. These findings show that the proposed techniques successfully detect surface cracks and deformations and can provide practical solutions to real-world problems. In addition, the developed methods can contribute as a tool for BIM platforms in smart cities for building health.
- MakaleAçık Erişim2025Advanced brain tumor analysis: a novel strategy for segmentation and classification using modern computational methods
Reis, Hatice Catal; Turk, Veysel
Despite their low incidence, brain tumors are one of the most invasive cancer types, constituting a significant burden of death and disease in all age groups. Early and accurate diagnosis of brain tumors plays a vital role in reducing mortality rates. The heterogeneous nature of brain tumors and the diversity of tumor lesions may make it difficult for radiologists to make the right decision in the manual diagnosis process. This study proposes the use of machine learning methods for the classification of brain tumors (pituitary, meningioma, and glioma) and the use of metaheuristic algorithms graph theory, and random walker algorithms in the segmentation of brain tumors. The classification performed with the proposed method obtained an overall accuracy rate of 98.33%. In addition, the classification accuracy of 99.50%, 99.50%, 98.67%, and 99.00% was achieved for no tumor, pituitary, meningioma, and glioma, respectively. Experiments in the segmentation process show that metaheuristic algorithms and max-flow graph cut approach produce successful results.
- MakaleAçık Erişim2025Post-seismic structural assessment: advanced crack detection through complex feature extraction using pre-trained deep learning and machine learning integration
Reis, Hatice Catal; Turk, Veysel; Ustuner, Mustafa; Kaya Yildiz, Cagla Melisa; Tatli, Ramazan
Earthquakes can often cause significant damage to buildings. After an earthquake, experts/managers need to make quick and accurate damage assessments. Traditionally, manual analysis processes have been widely used in damage assessment studies. The fact that these methods are time-consuming and based on human observation leads to certain limitations in damage assessment studies. In recent years, artificial intelligence techniques such as deep learning and machine learning have frequently been preferred in damage detection studies, and significant success has been achieved. This study aimed to automatically detect cracks/damages in the buildings in Diyarbakir city after the February 6, 2023 Kahramanmaras, Turkey earthquake. Our experimental dataset was collected by the researchers and named Kahramanmaras-Diyarbakir Earthquake Building Crack Dataset (KDBECD-2023). The data set consists of four categories in terms of damage level: undamaged, slightly damaged, moderately damaged, and heavily damaged buildings. DenseNet201 deep learning architecture and popular machine learning algorithms, Support Vector Machine, Random Forest, Decision Tree, and K-Nearest Neighbors (KNN) were used to classify cracks at different damage levels. In the experimental phase, feature extraction was performed with the DenseNet201 architecture. In addition, dimensional reduction was applied with the Principal Component Analysis method to reduce the computational complexity of the proposed hybrid study. According to the experimental results, the DenseNet201-KNN hybrid model gave the most successful result with an accuracy value of 94.62%. The results of this study can make important contributions to decision makers and experts in detecting cracks and damages in buildings after an earthquake.
- MakaleAçık Erişim2025DSCIMABNet: A novel multi-head attention depthwise separable CNN model for skin cancer detection
Reis, Hatice Catal; Turk, Veysel
Skin cancer is a common type of cancer worldwide. Early diagnosis of skin cancer can reduce the risk of death by increasing treatment success. However, it is challenging for dermatologists or specialists because the symptoms are vague in the early stages and cannot be noticed by the naked eye. This study examines digital diagnostic techniques supported by artificial intelligence, focusing on early skin cancer detection and two methods have been proposed. In the first method, DSCIMABNet deep learning architecture was developed by combining multi-head attention and depthwise separable convolution techniques. This model provides flexibility in learning the dataset's local features, abstract concepts, and long-term relationships. The DSCIMABNet model and modern deep learning models trained on ImageNet are proposed to be combined with the ensemble learning method in the second method. This approach provides a comprehensive feature extraction process that will increase the performance of the classification process with ensemble learning. The proposed approaches are trained and evaluated on the ISIC 2018 dataset with image enhancement applied in preprocessing. In the experimental results, DSCIMABNet achieved 84.28% accuracy, while the proposed hybrid method achieved 99.40% accuracy. Moreover, on the Mendeley dataset (CNN for Melanoma Detection Data), DSCIMABNet achieved 92.58% accuracy, while the hybrid method achieved 99.37% accuracy. This study may significantly contribute to developing new and effective methods for the early diagnosis and treatment of skin cancer.
- MakaleAçık Erişim2024Fusion of transformer attention and CNN features for skin cancer detection
Reis, Hatice Catal; Turk, Veysel
Melanoma is a skin cancer that spreads quickly and has serious risks. Early diagnosis is essential, but since the symptoms of skin lesions in the early stages are vague and similar, they can be difficult for specialists to detect. Therefore, machine learning-based alternative diagnostic methods can be developed in addition to existing ones. This study proposes a new deep learning model, a modified lightweight vision transformer (ViT) architecture, and a hybrid framework developed with an integrated deep learning model and an Ensemble Learning (EL) model for the early-stage diagnosis of skin lesions. The proposed deep learning model was developed based on convolution layers and transformers. The model is called multi-head attention block depthwise separable convolution network (MABSCNET). The proposed hybrid framework was developed by combining modern deep learning and EL models pre-trained with the ImageNet dataset along with the MABSCNET model. In the experimental process, the effectiveness of the proposed methods was evaluated on the ISIC 2020 dataset. Additionally, additional experiments were conducted on ISIC 2018 and a Kaggle dataset to analyze the proposed hybrid framework's classification performance. Image enhancement techniques were used in the datasets. In the ISIC 2020 dataset, the MABSCNET model reached 78.63 % accuracy, the ViT model obtained 76.50 %, and the hybrid framework reached 92.74 % accuracy. Moreover, the proposed hybrid framework achieved 100 % on the ISIC 2018 dataset and 94.24 % on the Kaggle dataset.
- MakaleAçık Erişim2024Integration of a CNN-based model and ensemble learning for detecting post-earthquake road cracks with deep features
Reis, Hatice Catal; Turk, Veysel; Karacur, Soner; Kurt, Ahmet Melih
A safe and healthy infrastructure is essential for humanity. Maintenance and repair of roads, which are of great importance, especially in transportation, is essential. Roads can sometimes be damaged due to natural disasters or human factors. In this study, roads damaged as a result of the natural disaster and the 6 February 2023 Gaziantep-Kahramanmaras earthquakes, which had devastating/fatal consequences, were studied. In recent years, the use of machine learning methods in the automatic detection process of road cracks has become widespread. Here, deep learning architectures generally based on multilayer convolutional neural networks have become a popular solution, mainly thanks to their high discrimination power in visual data. In this study, it was aimed to automatically detect cracks on the roads in Gaziantep (Nurdagi) and Adiyaman provinces after the earthquake. The data set used in the experimental process consists of two categories: uncracked and cracked, and was collected by the researchers. ResNet152 deep learning architecture, traditional machine learning algorithms, and an ensemble learning (EL) model were used to classify cracks. Among the machine learning algorithms used in the experiments are support vector machine (SVM), K-nearest neighbors, Adaptive Boosting, and Naive Bayes. In the experimental process, ResNet152 architecture was used for feature extraction, and classification was carried out with machine learning algorithms. In addition, the EL method has been used to improve the success of machine learning algorithms. According to experimental results, the SVM algorithm and EL method gave the most successful results with 98.68% accuracy values. The proposed method can benefit experts in the automatic classification of road cracks. The study can contribute to activating rapid coordination and transportation networks in natural disasters that cause fatal and destructive causes, such as earthquakes.
- MakaleAçık Erişim2023An Automatic Approach for Bone Tumor Detection from Non-Standard CT Images
Reis, Hatice Catal; Bayram, Bulent
Image processing techniques are applied in many fields of science. This study aims to detect tumors in the foot and create 3D models via computed tomography (CT), as well as to produce biometric data. 1 039 CT images were obtained from a server. The parameters used were a collimation of 64 detectors, a scanning thickness of 0,5-3 mm, and a pixel size of 512 x 512, with a radiometric resolution of the 16-bit gray levels. Noise reduction, segmentation, and morphological analysis were performed on CT scans to detect bone tumors. In addition, this study used digital image processing techniques to create a virtual three-dimensional (3D) model of bone tumors. The performance of our proposal was evaluated by analyzing the receptor operating characteristics (ROC). According to the results, the sensitivity, specificity, and precision in tumor detection were 0,96, 1, and 0,98%, respectively, with a 0,99% average F-measure. Radiologist reports were used for the sake of comparison. The proposed technique for detecting bone tumors of the foot via CT can help radiologists with its increased precision, sensitivity, specificity, and F-measure. This method could improve the diagnosis of foot and ankle tumors by allowing for the multidirectional quantification of abnormalities.
- MakaleAçık Erişim2023Transfer Learning Approach and Nucleus Segmentation with MedCLNet Colon Cancer Database
Reis, Hatice Catal; Turk, Veysel
Machine learning has been recently used especially in the medical field. In the diagnosis of serious diseases such as cancer, deep learning techniques can be used to reduce the workload of experts and to produce quick solutions. The nuclei found in the histopathology dataset are an essential parameter in disease detection. The nucleus segmentation was performed using the colorectal histology MNIST dataset for nucleus detection in this study. The graph theory, PSO, watershed, and random walker algorithms were used for the segmentation process. In addition, we present the 10-class MedCLNet visual dataset consisting of the NCT-CRC-HE-100 K dataset, LC25000 dataset, and GlaS dataset that can be used in transfer learning studies from deep learning techniques. The study proposes a transfer learning technique using the MedCLNet database. Deep neural networks pre-trained with the proposed transfer learning method were used in the classification with the colorectal histology MNIST dataset in the experimental process. DenseNet201, DenseNet169, InceptionResNetV2, InceptionV3, ResNet152V2, ResNet101V2, and Xception deep learning algorithms were used in transfer learning and the classification studies. The proposed approach was analyzed before and after transfer learning with different methods (DenseNet169 + SVM, DenseNet169 + GRU). In the performance measurement, using the colorectal histology MNIST dataset, 94.29% accuracy was obtained in the DenseNet169 model, which was initiated with random weights in the multi-classification study, and 95.00% accuracy after transfer learning was applied. In comparison with the results obtained from empirical studies, it was demonstrated that the proposed method produced satisfactory outcomes. The application is expected to provide a secondary evaluation for physicians in colon cancer detection and the segmentation. Graphical Abstract: [Figure not available: see fulltext.].
- MakaleYalnız künye2023Detection of forest fire using deep convolutional neural networks with transfer learning approach[Formula presented]
Reis, Hatice Catal; Turk, Veysel
Forest fires caused by natural causes such as climate change, temperature increase, lightning strikes, volcanic activity or human effects are among the world's most dangerous, deadly, and destructive disasters. Detection, prevention, and extinguishing forest fires is challenging. In addition, forest fires can cause habitat destruction that cannot be controlled in time and cause great material and moral losses. Therefore, fast and accurate Detection of forest fires is vital in emergency response. Here, in solving the problem, the transfer learning method from deep learning sub-topics can be used, which allows the application of pre-trained networks to a new problem. The Fire Luminosity Airborne-based Machine learning Evaluation dataset (consisting of forest fire images) obtained by Unmanned Aerial Vehicle was used in this study. In the Detection of forest fire images in the dataset, InceptionV3, DenseNet121, ResNet50V2, NASNetMobile, VGG-19 deep learning algorithms, transfer learning techniques that can produce more successful results than networks trained from scratch, and hybrid proposed with Support Vector Machine, Random Forest, Bidirectional Long Short-Term Memory, Gated Recurrent Unit algorithms methods have been applied. In the classification study with the Fire Luminosity Airborne-based Machine learning Evaluation dataset in performance measurement, 97.95% accuracy was obtained from the DenseNet121 model, which was started with random weights. In the transfer learning study using ImageNet weights, satisfactory results were obtained with 99.32% accuracy in the DenseNet121 model. We anticipate that working in forest fire detection and response can be entirely satisfactory.
- MakaleYayıncıda açık erişim2023MediNet: transfer learning approach with MediNet medical visual database
Reis, Hatice Catal; Turk, Veysel; Khoshelham, Kourosh; Kaya, Serhat
The rapid development of machine learning has increased interest in the use of deep learning methods in medical research. Deep learning in the medical field is used in disease detection and classification problems in the clinical decision-making process. Large amounts of labeled datasets are often required to train deep neural networks; however, in the medical field, the lack of a sufficient number of images in datasets and the difficulties encountered during data collection are among the main problems. In this study, we propose MediNet, a new 10-class visual dataset consisting of Rontgen (X-ray), Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, and Histopathological images such as calcaneal normal, calcaneal tumor, colon benign colon adenocarcinoma, brain normal, brain tumor, breast benign, breast malignant, chest normal, chest pneumonia. AlexNet, VGG19-BN, Inception V3, DenseNet 121, ResNet 101, EfficientNet B0, Nested-LSTM + CNN, and proposed RdiNet deep learning algorithms are used in the transfer learning for pre-training and classification application. Transfer learning aims to apply previously learned knowledge in a new task. Seven algorithms were trained with the MediNet dataset, and the models obtained from these algorithms, namely feature vectors, were recorded. Pre-training models were used for classification studies on chest X-ray images, diabetic retinopathy, and Covid-19 datasets with the transfer learning technique. In performance measurement, an accuracy of 94.84% was obtained in the traditional classification study for the InceptionV3 model in the classification study performed on the Chest X-Ray Images dataset, and the accuracy was increased 98.71% after the transfer learning technique was applied. In the Covid-19 dataset, the classification success of the DenseNet121 model before pre-trained was 88%, while the performance after the transfer application with MediNet was 92%. In the Diabetic retinopathy dataset, the classification success of the Nested-LSTM + CNN model before pre-trained was 79.35%, while the classification success was 81.52% after the transfer application with MediNet. The comparison of results obtained from experimental studies observed that the proposed method produced more successful results. Graphical abstract: [Figure not available: see fulltext.]
- MakaleAçık Erişim2022COVID-19 diagnosis with deep learning; [Diagnóstico de COVID-19 con Deep Learning]
Reis, Hatice Catal
The coronavirus disease 2019 (COVID-19) is fatal and spreading rapidly. Early detection and diagnosis of the COVID-19 infection will prevent rapid spread. This study aims to automatically detect COVID-19 through a chest computed tomography (CT) dataset. The standard models for automatic COVID-19 detection using raw chest CT images are presented. This study uses convolutional neural network (CNN), Zeiler and Fergus network (ZFNet), and dense convolutional network-121 (DenseNet121) architectures of deep convolutional neural network models. The proposed models are presented to provide accurate diagnosis for binary classification. The datasets were obtained from a public database. This retrospective study included 757 chest CT images (360 confirmed COVID-19 and 397 non-COVID-19 chest CT images). The algorithms were coded using the Python programming language. The performance metrics used were accuracy, precision, recall, F1-score, and ROC-AUC. Comparative analyses are presented between the three models by considering hyper-parameter factors to find the best model. We obtained the best performance, with an accuracy of 94,7%, a recall of 90%, a precision of 100%, and an F1-score of 94,7% from the CNN model. As a result, the CNN algorithm is more accurate and precise than the ZFNet and DenseNet121 models. This study can present a second point of view to medical staff.
- MakaleYalnız künye2022InSiNet: 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.]
- MakaleAçık Erişim2022COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images
Reis, Hatice Catal; Turk, Veysel
COVID-19 (SARS-CoV-2), which causes acute respiratory syndrome, is a contagious and deadly disease that has devastating effects on society and human life. COVID-19 can cause serious complications, especially in patients with pre-existing chronic health problems such as diabetes, hypertension, lung cancer, weakened immune systems, and the elderly. The most critical step in the fight against COVID-19 is the rapid diagnosis of infected patients. Computed Tomography (CT), chest X-ray (CXR), and RT-PCR diagnostic kits are frequently used to diagnose the disease. However, due to difficulties such as the inadequacy of RT-PCR test kits and false negative (FN) results in the early stages of the disease, the time-consuming examination of medical images obtained from CT and CXR imaging techniques by specialists/doctors, and the increasing workload on specialists, it is challenging to detect COVID-19. Therefore, researchers have suggested searching for new methods in COVID- 19 detection. In analysis studies with CT and CXR radiography images, it was determined that COVID-19-infected patients experienced abnormalities related to COVID-19. The anomalies observed here are the primary motivation for artificial intelligence researchers to develop COVID-19 detection applications with deep convolutional neural networks. Here, convolutional neural network-based deep learning algorithms from artificial intelligence technologies with high discrimination capabilities can be considered as an alternative approach in the disease detection process. This study proposes a deep convolutional neural network, COVID-DSNet, to diagnose typical pneumonia (bacterial, viral) and COVID-19 diseases from CT, CXR, hybrid CT + CXR images. In the multi-classification study with the CT dataset, 97.60 % accuracy and 97.60 % sensitivity values were obtained from the COVID-DSNet model, and 100 %, 96.30 %, and 96.58 % sensitivity values were obtained in the detection of typical, common pneumonia and COVID-19, respectively. The proposed model is an economical, practical deep learning network that data scientists can benefit from and develop. Although it is not a definitive solution in disease diagnosis, it may help experts as it produces successful results in detecting pneumonia and COVID-19.
- MakaleAçık Erişim2021Automatic Classification of COVID-19 using CT-Scan Images
Reis, Hatice Catal
Medicine and engineering sciences have been working in close contact for common purposes. Machine learning algorithms are used in the medical field for early diagnosis prediction. The major aim of this study is to evaluate machine learning algorithms and deep learning algorithms using computed tomography scan (CT-scan) images for automated detection of the coronavirus disease 2019 (COVID-19) patients. We obtained seven hundred and fifty-seven (757) CT-scan images from a public platform. We applied four automated traditional classification methods to predict COVID-19 using deep learning and machine learning. These algorithms are SVM, AdaBoost, NASNetMobile, and InceptionV3. Comparative analyses are presented among the four models by considering metric performance factors to find the best model. The results show that the InceptionV3 model achieves better performance in terms of accuracy, precision, recall, Cohen’s kappa, F1-score, root mean squared error (RMSE), and receiver operating characteristic-area under the curve (ROC-AUC), in comparison with the other Covid-19 classifiers. Accordingly, the InceptionV3 approach is recommended for the automatic diagnosis of Covid-19 and assessments. This research can present a second point of view to medical experts and it can save time for researchers as the performance of standard machine learning methods in detecting COVID-19 is evaluated.
- MakaleYalnız künye2021ReCRNet: a deep residual network for crack detection in historical buildings
Reis, Hatice Catal; Khoshelham, Kourosh
In historical buildings, surface cracks are important indicators of potential structural damage. Natural disasters and indirect human factors, which are frequently encountered in recent periods, negatively affect historical buildings and structures. Fast and cost-effective crack detection methods play a key role in structural health monitoring of historical buildings. This paper presents methodologies for identifying concrete cracks using deep learning. We propose ReCRNet, a deep learning architecture designed for classifying images of cracks. The performance of the proposed method is evaluated and compared with state-of-the-art methods such as AlexNet, VGG 19, linear support vector machine (SVM), and decision tree (DT). The results show that ReCRNet achieves better performance in terms of accuracy, precision, recall, F1-score, and receiver operating characteristic (ROC), in comparison with the other crack classifiers. Accordingly, the proposed approach is recommended for automatic monitoring of historical buildings and building condition assessments.
- MakaleYalnız künye2020Light rail line design for East Black Sea region: A case study of Trabzon
Reis, Hatice Catal; Birinci, Abdulkadir
Growing cities need to improve transportation in response to the accessibility needs of ever-expanding urban and metropolitan populations. Metropolitan Municipalities have to find low-cost, nature friendly, bus/train/subway transfer, and socially responsible transportation solutions that can meet the accessibility needs of urban residents and assist future economic development in urban areas. In this study, the light rail network was designed for the province of Trabzon by using orthophoto and Landsat satellite images. While this light rail network is being constructed, many criteria such as regional population density, slope, minimum service interval, integration with other transportation networks, geological structure, and proximity to public institutions and organizations have been used together. These criteria are shown with a GIS-based design model. Analysis maps were created by using the conditions/criteria and synthesis maps used in the design model. The 102 km route features 205 stops, extending from Of along to Besikduzu. A combined bus and light rail network will significantly improve public transport access to major public institutions and organizations, sporting and entertainment facilities, universities, schools, health precincts, bazaars, shopping malls. It is foreseen that the light rail system will provide the most suitable, modern, fast, comfortable and safe transportation in the long term considering the number of passengers and many other criteria. This light rail system designed for the province of Trabzon is thought to be a pioneering work for the Black Sea region through the interpretation of the resulting map and route networks.
- MakaleYalnız künye2018Open source library-based 3D face point cloud generation
Bayram, Bulent; Ozkan, Taskin; Reis, Hatice Catal; Bakirman, Tolga; Cetin, Ibrahim; Seker, Dursun Zafer
Three dimensional (3D) face and body modeling is widely used in various fields such as plastic surgery, diagnosis of facial or body anomalies, 3D computer games and 3D simulation software. Since, commercial 3D face and body scanners are usually expensive, an alternative solution with lower cost is highly desirable. The objective of this study is to create 3D facial point cloud using Semi Global Image Matching method with minimum number of images utilizing a cost effective method. A non-metric Canon 600D camera with 18 megapixels resolution (3456 × 5184) and 60 mm macro lens have been used for face imaging that have been taken from a distance of 120 cm. Five faces have been modeled by the developed algorithm and scanned by David SLS-2 structured light system for accuracy assessment. Open source Cloud Compare software has been used for comparing the results of proposed method with the structured light system. The mean accuracy of five faces obtained as 90.5%. It has been observed that illumination conditions, uncontrolled movements of face or body, hair and eyebrow have negative impacts on the obtained results. The sufficiency of Semi global image matching method has been tested to create dense point cloud data from three stereo pairs for 3D facial modelling.