Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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    Investigating Transfer Learning Performances of Deep Learning Models for Classification of GPR B-Scan Images
    (2022) Dikmen, Mehmet
    Recent advances in deep learning models have made them the state-of-the art method for image classification. Due to this success, they have been applied to many areas, such as satellite image processing, medical image interpretation, video processing, etc. Recently, deep learning models have been utilized for processing Ground Penetrating Radar (GPR) data as well. However, studies general focus on building new Convolutional Neural Network (CNN) models instead of utilizing baseline ones. This paper investigates the usefulness of existing baseline CNN models for classifying GPR B-scan images and aims to determine how well pre-trained models perform. To that end, a real bridge deck GPR data, DECKGPRHv1.0 dataset was used to evaluate the transfer learning performances of various CNN models. Different variants of the models in terms of varying depths and number of parameters were also considered and evaluated in a comparative manner. Although it is an older model, ResNet achieved the best results with 0.998 accuracy. The experimental results showed that there is generally a direct correlation between the simplicity of the model and its success. Overall, it is concluded that near perfect results are possible by just adapting pretrained models to the problem without fine-tuning.
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    Classification of Canine Maturity and Bone Fracture Time Based on X-Ray Images of Long Bones
    (2021) Ergun, Gulnur Begum; Guney, Selda; 0000-0002-0573-1326; 0000-0001-8469-5484
    Veterinarians use X-rays for almost all examinations of clinical fractures to determine the appropriate treatment. Before treatment, vets need to know the date of the injury, type of the broken bone, and age of the dog. The maturity of the dog and the time of the fracture affects the approach to the fracture site, the surgical procedure and needed materials. This comprehensive study has three main goals: determining the maturity of the dogs (Task 1), dating fractures (Task 2), and finally, detecting fractures of the long bones in dogs (Task 3). The most popular deep neural networks are used: AlexNet, ResNet-50 and GoogLeNet. One of the most popular machine learning algorithms, support vector machines (SVM), is used for comparison. The performance of all sub-studies is evaluated using accuracy and F1 score. Each task has been successful with different network architecture. ResNet-50, AlexNet and GoogLeNet are the most successful algorithms for the three tasks, with F1 scores of 0.75, 0.80 and 0.88, respectively. Data augmentation is performed to make models more robust, and the F1 scores of the three tasks were 0.80, 0.81, and 0.89 using ResNet-50, which is the most successful model. This preliminary work can be developed into support tools for practicing veterinarians that will make a difference in the treatment of dogs with fractured bones. Considering the lack of work in this interdisciplinary field, this paper may lead to future studies.