Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14627/7
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Article Comparative Evaluation of Vision Transformers and Convolutional Networks for Breast Ultrasound Image Classification(Open Exploration Publishing Inc, 2026) Naral S.; Cakmak Y.; Pacal I.; Pacal, Ishak; Cakmak, Yigitcan; Naral, SuleymanAim: Interobserver variability continues to limit the consistency of breast ultrasound interpretation. This study compares two Vision Transformer (ViT) models and two Convolutional Neural Network (CNN) models for automated three-class breast ultrasound classification, with a specific focus on the tradeoff between predictive performance and computational efficiency. Methods: Swin Transformer Base and DeiT Base were evaluated alongside InceptionV3 and MobileNetV3 Large using the public Breast Ultrasound Images (BUSI) dataset, which contains 780 images labeled as benign, malignant, and normal. A consistent on-the-fly augmentation pipeline was applied during training to promote robustness and reduce sensitivity to incidental image variations. Results: Swin Transformer Base achieved the highest test accuracy (0.9167) and F1 score (0.8981). MobileNetV3 Large reached an accuracy of 0.8583 with substantially lower computational demand. The efficiency contrast was pronounced, with Swin requiring 30.33 GFLOPs versus 0.43 GFLOPs for MobileNetV3 Large. Conclusions: On this benchmark, ViT models can yield higher classification performance, while lightweight CNNs offer a strong efficiency profile that may better match deployment-constrained settings. These results suggest that model selection should be guided by both predictive accuracy and operational feasibility within the target clinical workflow. © The Author(s) 2026.Article Citation - WoS: 11Citation - Scopus: 23Improving Yolo Detection Performance of Autonomous Vehicles in Adverse Weather Conditions Using Metaheuristic Algorithms(Mdpi, 2024) Ozcan, Ibrahim; Altun, Yusuf; Parlak, CevahirDespite the rapid advances in deep learning (DL) for object detection, existing techniques still face several challenges. In particular, object detection in adverse weather conditions (AWCs) requires complex and computationally costly models to achieve high accuracy rates. Furthermore, the generalization capabilities of these methods struggle to show consistent performance under different conditions. This work focuses on improving object detection using You Only Look Once (YOLO) versions 5, 7, and 9 in AWCs for autonomous vehicles. Although the default values of the hyperparameters are successful for images without AWCs, there is a need to find the optimum values of the hyperparameters in AWCs. Given the many numbers and wide range of hyperparameters, determining them through trial and error is particularly challenging. In this study, the Gray Wolf Optimizer (GWO), Artificial Rabbit Optimizer (ARO), and Chimpanzee Leader Selection Optimization (CLEO) are independently applied to optimize the hyperparameters of YOLOv5, YOLOv7, and YOLOv9. The results show that the preferred method significantly improves the algorithms' performances for object detection. The overall performance of the YOLO models on the object detection for AWC task increased by 6.146%, by 6.277% for YOLOv7 + CLEO, and by 6.764% for YOLOv9 + GWO.