Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2025Advanced Classification of Poxvirus-Based Skin Diseases Using Deep Learning Techniques
Arik, Kaan; Agdas, Mehmet T.; Korkmaz, Adem; Kosunalp, Selahattin; Iliev, Teodor
Viral infections, especially those of the poxvirus family, present significant diagnostic challenges due to their similar clinical symptoms. This study proposes an innovative deep learning-based approach to classify six categories of poxvirus-related skin diseases: chickenpox, cowpox, healthy, measles, monkeypox, and smallpox. A dataset of 9,120 augmented images was used to train, validate, and test three advanced deep-learning models-YOLOv8, YOLOv5, and ResNet32. Among the models, YOLOv8 demonstrated superior performance, achieving an accuracy of 99.80%, precision of 99.28%, and recall of 99.14%, significantly outperforming YOLOv5 and ResNet32. The results underscore the potential of YOLOv8 in medical image analysis, providing a robust and efficient tool for the early detection and accurate classification of viral skin diseases. Comparisons with related studies highlight the effectiveness of the proposed approach, making it a state-of-the-art solution for improving diagnostic accuracy in healthcare. Future work will focus on extending the dataset and evaluating the model's applicability in real-time clinical environments.
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İncele - MakaleAçık Erişim2024Scopus SJRQ2Detection of Threats to Farm Animals Using Deep Learning Models: A Comparative Study
Korkmaz, Adem; Agdas, Mehmet Tevfik; Kosunalp, Selahattin; Iliev, Teodor; Stoyanov, Ivaylo
The increasing global population and environmental changes pose significant challenges to food security and sustainable agricultural practices. To overcome these challenges, protecting farm animals and effectively detecting potential environmental threats is critical for economic and ecological sustainability. In this context, the current study examined the animal detection capabilities and efficiency of advanced deep learning models, such as YOLOv8, Yolo-NAS, and Fast-RNN, across a dataset of 2462 images encompassing various animal species that could pose a risk to farm animals. After converting the images into a standardized format, they were divided into three sets for training, validation, and testing, and each model was evaluated on this dataset during the analysis process. The findings indicated that the YOLOv8 model demonstrated superior performance, with 93% precision, 85.2% recall, and 93.1% mAP50 values, while Yolo-NAS was particularly noteworthy for its high recall value, indicating a remarkable detection ability. The Fast-RNN model also offered significant efficiency with balanced performance. The results reveal the considerable potential of deep learning-based object detection technologies in protecting farm animals and enhancing farm security. Additionally, this study provides valuable insights for future model optimization and customization research.
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İncele - MakaleAçık Erişim2023Deep Learning-Based Automatic Helmet Detection System in Construction Site Cameras
Korkmaz, Adem; Ağdaş, Mehmet Tevfik
Ensuring worker safety in high-risk environments such as construction sites is of paramount importance. Personal protective equipment, particularly helmets, plays a critical role in preventing severe head injuries. This study aims to develop an automated helmet detection system using the state-of-the-art YOLOv8 deep learning model to enhance safety monitoring in real-time. The dataset used for the study consists of 16,867 images, with various data augmentation and preprocessing techniques applied to improve the model's robustness. The YOLOv8 model achieved a 96.9% mAP50 score, outperforming other deep learning models in similar studies. The results demonstrate the effectiveness of the YOLOv8 model for accurate and efficient helmet detection in construction sites, paving the way for improved safety monitoring and enforcement in the construction industry.
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