Yazar "Ozdemir, Cuneyt" seçeneğine göre listele
Listeleniyor 1 - 14 / 14
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe A New Approach for Plotting Raster Based Image Files(IEEE, 2014) Dogan, Yahya; Atas, Musa; Ozdemir, CuneytIn this study a new approach that is used for plotting raster based image files, is proposed. Proposed method is tested on the prototype plotter which is designed as a Cartesian-robot architecture. Work list is produced by the proposed edge tracking algorithm on the canny edge detector based images. Outputs are generated from the work list as a converted atomic move commands which can be interpreted by Cartesian-robot. Edge images by using conventional vectorization methods are compared with outputs of the proposed algorithm. It is observed that proposed method is superior to other techniques, in terms of representing original images.Öğe A new approach for plotting raster based image files(IEEE Computer Society, 2014) Dogan, Yahya; Atas, Musa; Ozdemir, CuneytIn this study a new approach that is used for plotting raster based image files, is proposed. Proposed method is tested on the prototype plotter which is designed as a Cartesian-robot architecture. Work list is produced by the proposed edge tracking algorithm on the canny edge detector based images. Outputs are generated from the work list as a converted atomic move commands which can be interpreted by Cartesian-robot. Edge images by using conventional vectorization methods are compared with outputs of the proposed algorithm. It is observed that proposed method is superior to other techniques, in terms of representing original images. © 2014 IEEE.Öğe A new local pooling approach for convolutional neural network: local binary pattern(Springer, 2023) Ozdemir, Cuneyt; Dogan, Yahya; Kaya, YilmazThe pooling layer used in CNN models aims to reduce the resolution of image/feature maps while retaining their distinctive information, reducing computation time and enabling deeper models. Max and average pooling methods are frequently used in CNN models due to their computational efficiency; however, these methods discard the position information of the pixels. In this study, we proposed an LBP-based pooling method that generates a neighborhood-based output for any pixel, reflecting the correlation between pixels in the local area. Our proposed method reduces information loss since it considers the neighborhood and size of the pixels in the pooling region. Experimental studies were performed on four public datasets to assess the effectiveness of the LBP pooling method. In experimental studies, a toy CNN model and various transfer learning models were utilized in conducting test operations. The proposed method provided improvements of 1.56% for Fashion MNIST, 0.22% for MNIST, 3.95% for CIFAR10, and 5% for CIFAR100 dataset using the toy model. In the experimental studies conducted using the transfer learning model, performance improvements of 6.99(-/+)(0.74) and 8.3(-/+)(0.1) were achieved for CIFAR10 and CIFAR100, respectively. We observed that the proposed method outperforms the commonly used pooling layers in CNN models. Code for this paper can be publicly accessed at: https://github.com/cuneytozdemir/lbppoolingÖğe A Novel Similarity Algorithm for Fixing Erroneous Turkish Text and Detection of Roots(IEEE, 2014) Ozdemir, Cuneyt; Atas, MusaFinding roots of words is widely used in document classification and text mining. Computational methods of text similarity are intensely utilized on the English words and successful outcomes are obtained. On the other hand, applying the aforementioned methods on the Turkish words did not give the similar success. In this study, a novel similarity computation algorithm is developed. By using this algorithm it is aimed to find correct words or advice possible alternatives from the written erroneous Turkish words as a highest accuracy rate.Öğe Adapting transfer learning models to dataset through pruning and Avg-TopK pooling(Springer London Ltd, 2024) Ozdemir, CuneytThis study focuses on efficiently adapting transfer learning models to address the challenges of creating customized deep learning models for specific datasets. Designing a model from scratch can be time-consuming and complex due to factors like model complexity, size, and dataset structure. To overcome these obstacles, a novel approach is proposed using transfer learning models. The proposed method involves identifying relevant layers in transfer learning models and removing unnecessary ones using a layer-based variance pruning technique. This results in the creation of new models with improved computational efficiency and classification performance. By streamlining the models through layer-based variance pruning, the study achieves enhanced accuracy and faster computation. Experiments were conducted using the COVID-19 dataset and well-known transfer learning models, including InceptionV3, ResNet50V2, DenseNet201, VGG16, and Xception to validate the approach. Among these models, the variance-based layer pruning technique was applied to InceptionV3 and DenseNet201, yielding the best results. When these pruned models were combined with the new pooling layer, Avg-TopK, the proposed method achieved an outstanding image classification accuracy of 99.3%. Comparisons with previous models and literature studies indicate that the proposed approach outperforms existing methods, showcasing state-of-the-art performance. This high-performance approach provides great potential for diagnosing COVID-19 and monitoring disease progression, especially on hardware-limited devices. By leveraging transfer learning models, pruning, and efficient pooling techniques, the study presents a promising strategy for tackling challenges in custom model design, leading to exceptional results in such as image classification and segmentation tasks. The proposed methodology holds the potential to yield exceptional outcomes across a spectrum of tasks, encompassing disciplines such as image classification and segmentation.Öğe Advancing brain tumor classification through MTAP model: an innovative approach in medical diagnostics(Springer Heidelberg, 2024) Ozdemir, Cuneyt; Dogan, YahyaThe early diagnosis of brain tumors is critical in the area of healthcare, owing to the potentially life-threatening repercussions unstable growths within the brain can pose to individuals. The accurate and early diagnosis of brain tumors enables prompt medical intervention. In this context, we have established a new model called MTAP to enable a highly accurate diagnosis of brain tumors. The MTAP model addresses dataset class imbalance by utilizing the ADASYN method, employs a network pruning technique to reduce unnecessary weights and nodes in the neural network, and incorporates Avg-TopK pooling method for enhanced feature extraction. The primary goal of our research is to enhance the accuracy of brain tumor type detection, a critical aspect of medical imaging and diagnostics. The MTAP model introduces a novel classification strategy for brain tumors, leveraging the strength of deep learning methods and novel model refinement techniques. Following comprehensive experimental studies and meticulous design, the MTAP model has achieved a state-of-the-art accuracy of 99.69%. Our findings indicate that the use of deep learning and innovative model refinement techniques shows promise in facilitating the early detection of brain tumors. Analysis of the model's heat map revealed a notable focus on regions encompassing the parietal and temporal lobes.Öğe Advancing early diagnosis of Alzheimer's disease with next-generation deep learning methods(Elsevier Sci Ltd, 2024) Ozdemir, Cuneyt; Dogan, YahyaAlzheimer's disease, characterized by cognitive decline and memory impairment, poses a significant healthcare challenge. This study presents a specially designed CNN model, utilizing contemporary approaches, to distinguish between various types of Alzheimer's disease. This model can serve as an early diagnostic tool to prevent the disease from progressing towards more pronounced and severe dementia symptoms. In this context, the performance of various transfer learning models has been examined, leading to the development of a specialized model integrating compression and excitation blocks, an innovative Avg-TopK pooling layer, and the SMOTE technique to handle data imbalance. The ablation study results demonstrate the critical role of these components, highlighting the model's effectiveness and innovative design. This study is novel in that it combines modern methodologies for detecting Alzheimer's disease, resulting in a model with state-of-the-art accuracy of 99.84% and improved computing efficiency. Grad-CAM analysis further demonstrates that the model focuses on cortical areas during classification, underscoring its potential as a robust diagnostic tool. These innovations represent a significant advancement over existing models, positioning this study as a pioneering effort in the early diagnosis of Alzheimer's disease. This study aims to contribute significantly to both academic research and medical applications by focusing on integrating artificial intelligence methodologies into medical diagnosis.Öğe Age Estimation from Left-Hand Radiographs with Deep Learning Methods(Int Information & Engineering Technology Assoc, 2021) Ozdemir, Cuneyt; Gedik, Mehmet Ali; Kaya, YilmazBone age is estimated in pediatric medicine for medical and legal purposes. In pediatric medicine, it aids in the growth and development assessment of various diseases affecting children. In forensic medicine, it is required to determine criminal liability by age, refugee age estimation, and child-adult discrimination. In such cases, radiologists or forensic medicine specialists conduct bone age estimation from left hand-wrist radiographs using atlas methods that require time and effort. This study aims to develop a computer-based decision support system using a new modified deep learning approach to accelerate radiologists' workflow for pediatric bone age estimation from wrist radiographs. The KCRD dataset created by us was used to test the proposed method. The performance of the proposed modified IncepitonV3 model compared to IncepitonV3, MobileNetV2, EfficientNetB7 models. Acceptably high results (MAE=4.3, RMSE=5.76, and R-2=0.99) were observed with the modified IncepitonV3 transfer deep learning method.Öğe Biometric identification using panoramic dental radiographic images with few-shot learning(Tubitak Scientific & Technological Research Council Turkey, 2022) Atas, Musa; Ozdemir, Cuneyt; Atas, Isa; Ak, Burak; Ozeroglu, EsmaDetermining identity is a crucial task especially in the cases of mass disasters such as tsunamis, earthquakes, fires, epidemics, and in forensics. Although there are various studies in the literature on biometric identification from radiographic dental images, more research is still required. In this study, a panoramic dental radiographic (PDR) image -based human identification system was developed using a customized deep convolutional neural network model in a few-shot learning scheme. The proposed model (PDR-net) was trained on 600 PDR images obtained from a total of 300 patients. As the PDR images of the patients were very different in terms of pose and intensity, they were first cropped by the domain experts according to the region of interest and adjusted to standard view with histogram equalization. A customized data augmentation approach was applied in order for the model to generalize better while it was being trained. The proposed model achieved a prediction accuracy of 84.72% and 97.91% in Rank-1 and Rank-10, respectively, by testing 144 PDR images of 72 patients that had not been previously used in training. It was concluded that well known similarity metrics such as Euclidean, Manhattan, Cosine, Pearson, Kendall's Tau and sum of absolute difference can be utilized in few-shot learning. Moreover, Cosine and Pearson similarity achieved the highest Rank 1 score of 84.72%. It was observed that as the number of rank increased, the Spearman and Kendall's Tau metrics had the same success as Cosine and Pearson. Based on the superimposed heatmap image analysis, it was determined that the maxillary, mandibular, nasal fossa, sinus and other bone forms in the mouth contributed biometric identification. It was also found that customized data augmentation parameters contributed positively to biometric identification.Öğe Classification of Brain Tumors from MR Images Using a New CNN Architecture(Int Information & Engineering Technology Assoc, 2023) Ozdemir, CuneytAccurately classifying brain tumors is a crucial factor in combatting, intervening , treating the disease. By automating the tumor diagnosis process without the involvement of human factors, it is possible to decrease the occurrence of human errors during the diagnosis process. In a new deep convolutional neural network architecture was developed to tackle the brain tumor classification problem, resulting in the successful classification of three distinct types of brain tumors -meningioma, glioma , pituitary. With the propose CNN architecture, a classification accuracy of 98.69% was achieved in brain tumor classification. The recommend model is simple and very fast. It was observed that giving high kernel size and strides values in the first layers and low values in the middle layers of the convolutional layers, and keeping the strides value small in the pooling layer had greatly increased on the model performance. The recommend CNN architecture was compared with studies using the same dataset and transfer learning models in the literature. As a result of these comparisons, high-scoring results were obtained with the recommend model. The classification success achieved by the model is state-of-the-art among stand-alone models.Öğe CLASSIFICATION OF TURKISH SPAM E-MAILS WITH ARTIFICIAL IMMUNE SYSTEM(IEEE, 2013) Ozdemir, Cuneyt; Atas, Musa; Ozer, Ahmet BedriIn this study, it is aimed to detect frequently encountered spam e-mails with artificial immune algorithms. Turkish spam and non-spam e-mail dataset are generated within the scope of the work. Fisher discriminant analysis (FDA) and Euclidean Distance (ED) are utilized in order to extract features from the turkish email dataset. In order to evaluate the classification accuracies, artificial immune algorithms with Bayes as a linear and artificial neural network as a non-linear classifiers are used. Various artificial immune algorithms, including AIRS1, AIRS2, AIRS2PARALLEL, CLONALG and CSCA are investigated. Among them, CSCA reveals the best classification accuracy of 86%. Furthermore, CSCA algorithm classifies spam emails with 81% and non-spam e-mails with 90% accuracies.Öğe Enhancing CNN model classification performance through RGB angle rotation method(Springer Science and Business Media Deutschland GmbH, 2024) Dogan, Yahya; Ozdemir, Cuneyt; Kaya, YılmazIn recent years, convolutional neural networks have significantly advanced the field of computer vision by automatically extracting features from image data. CNNs enable the modeling of complex and abstract image features using learnable filters, eliminating the need for manual feature extraction. However, combining feature maps obtained from CNNs with different approaches can lead to more complex and interpretable inferences, thereby enhancing model performance and generalizability. In this study, we propose a new method called RGB angle rotation to effectively obtain feature maps from RGB images. Our method rotates color channels at different angles and uses the angle information between channels to generate new feature maps. We then investigate the effects of integrating models trained with these feature maps into an ensemble architecture. Experimental results on the CIFAR-10 dataset show that using the proposed method in the ensemble model results in performance increases of 9.10 and 8.42% for the B and R channels, respectively, compared to the original model, while the effect of the G channel is very limited. For the CIFAR-100 dataset, the proposed method resulted in a 17.09% improvement in ensemble model performance for the R channel, a 5.06% increase for the B channel, and no significant improvement for the G channel compared to the original model. Additionally, we compared our method with traditional feature extraction methods like scale-invariant feature transform and local binary pattern and observed higher performance. In conclusion, it has been observed that the proposed RGB angle rotation method significantly impacts model performance. © The Author(s) 2024.Öğe GENDER IDENTIFICATION FROM LEFT HAND-WRIST X-RAY IMAGES WITH A HYBRID DEEP LEARNING METHOD(Konya Teknik Univ, 2023) Ozdemir, Cuneyt; Gedik, Mehmet Ali; Kucuker, Hudaverdi; Kaya, YilmazIn forensic investigations, characteristics such as gender, age, ethnic origin, and height are important in determining biological identity. In this study, we developed a deep learning-based decision support system for gender recognition from wrist radiographs using 13,935 images collected from individuals aged between 2 and 79 years. Differences in all regions of the images, such as carpal bones, radius, ulna bones, epiphysis, cortex, and medulla, were utilized. A hybrid model was proposed for gender determination from X-ray images, in which deep metrics were combined in appropriate layers of transfer learning methods. Although gender determination from X-ray images obtained from different countries has been reported in the literature, no such study has been conducted in Turkey. It was found that gender discrimination yielded different results for males and females. Gender identification was found to be more successful in females aged between 10 and 40 years than in males. However, for age ranges of 2-10 and 40-79 years, gender discrimination was found to be more successful in males. Finally, heat maps of the regions focused on by the proposed model were obtained from the images, and it was found that the areas of focus for gender discrimination were different between males and females.Öğe RGB-Angle-Wheel: A new data augmentation method for deep learning models(Elsevier, 2024) Ozdemir, Cuneyt; Dogan, Yahya; Kaya, YilmazDeep learning models often rely on a diverse and well -augmented dataset for optimal performance. In this context, the methods of data augmentation are pivotal in boosting the models' ability to generalize. In this paper, we introduce a novel data augmentation method, which we call RGB-Angle-Wheel, to improve the performance of deep learning models on RGB format images. This method involves rotating each color channel at specific angles to generate new training data that is distinct from the original dataset but shares similar properties. Experimental results on the CIFAR-10, CIFAR-100,and COCO datasets have validated the efficacy of the proposed method for enhancing model performance. Specifically, certain transformations in the red (R) and blue (B) channels improve model accuracy significantly, whereas the effect on the green (G) channel remains limited. These results indicate that the careful selection of transformation parameters plays a critical role in enhancing model performance. The findings of the study indicate that the proposed method can be utilized specifically for image processing, image classification, object detection, and other deep learning applications. Experiments demonstrate that the proposed method improves the model's efficacy and generalizability.
