Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2025/r/munzur/4120
  Advanced 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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  AtıfOpenAlex1

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• MakaleAçık Erişim2022Scopus SJRQ3/r/munzur/3023
  A New Rail Surface Defects Detection Approach Using 3D Laser Cameras Based on ResNet50

  Santur, Yunus; Yilmazer, Merve; Karakose, Mehmet; Akin, Erhan

  Rail transportation systems, which are used as one of the most common means of transportation worldwide, should be regularly inspected to prevent accidents that may occur. The rail condition monitoring can be performed in high accuracy and real time using computer vision, deep learning algorithms today. In this study, a new deep learning based approach using 3D laser cameras for rail inspection is presented. In the proposed approach, two 3D laser cameras placed on a real train, seeing the rail line from the left and right surfaces were used. These data consisting of sensitive distance value constitute the input data of the ResNet50 transfer learning model. The training was carried out on Nvidia Cuda supported graphics processing units using ResNet50 Convolutional Neural Network. During the test phase, the operation speed and accuracy rate of the method was measured by repeating the process on real-time rail profiles. The accuracy rate was calculated as 94%. As a result a new approach is presented based on deep learning using 3D laser cameras for rail inspection is presented.

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  AtıfScopus12·WoS8·OpenAlex9

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