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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.
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    A Novel Deep Learning Algorithm for the Automatic Detection of High-Grade Gliomas on T2-Weighted Magnetic Resonance I mages: A Preliminary Machine Learning Study
    (2020) Atici, Mehmet Ali; Sagiroglu, Seref; Celtikci, Pinar; Ucar, Murat; Borcek, Alp Ozgun; Emmez, Hakan; Celtikci, Emrah; 0000-0002-1655-6957; 31608975
    AIM: To propose a convolutional neural network (CNN) for the automatic detection of high-grade gliomas (HGGs) on T2-weighted magnetic resonance imaging (MRI) scans. MATERIAL and METHODS: A total of 3580 images obtained from 179 individuals were used for training and validation. After random rotation and vertical flip, training data was augmented by factor of 10 in each iteration. In order to increase data processing time, every single image converted into a Jpeg image which has a resolution of 320x320. Accuracy, precision and recall rates were calculated after training of the algorithm. RESULTS: Following training, CNN achieved acceptable performance ratios of 0.854 to 0.944 for accuracy, 0.812 to 0.980 for precision and 0.738 to 0.907 for recall. Also, CNN was able to detect HGG cases even though there is no apparent mass lesion in the given image. CONCLUSION: Our preliminary findings demonstrate; currently proposed CNN model achieves acceptable performance results for the automatic detection of HGGs on T2-weighted images.