Yazar "Ari, Berna Gurler" seçeneğine göre listele

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    Accurate Segmentation of Brain Tumors in Magnetic Resonance Images with Pyramid Stage Decomposition Network Approach
    (Ieee, 2024) Ari, Berna Gurler; Uzen, Huseyin; Sengur, Abdulkadir
    This study explores the utilization of the Pyramid Scene Parsing Network (PSPNet) architecture to achieve accurate segmentation of brain tumors in magnetic resonance (MR) images. Experimental evaluations were conducted on different pre-trained backbone network models, including Vgg16, Inceptionv3, Mobilenetv2, Efficientnetb0, Resnet18, Resnet34, Resnet50, Resnet101, Resnext50, and Resnext101, assessing the performance of each model in brain tumor segmentation. The results highlight the VGG16-PSPNet model as the most successful, showcasing high F1-score, mIoU, precision, recall, and accuracy values.
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    Scene Text Recognition Using Permutated Autoregressive Sequence Model and YOLOv8
    (Ieee, 2024) Ari, Berna Gurler; Comert, Zafer
    Text is considered one of the most powerful sources for high-level information extraction. Scene text recognition, a hot field of study in computer vision, is based on first detecting text regions and then analyzing the text in these regions. We target multiple choice questions and question paper analysis, which are the most frequently used methods for measurement and evaluation at all levels in our country, as the target of our research. Three main goals were achieved while conducting experiments on the dataset created with different people and marking styles. These objectives realized on the question paper are respectively; Determining the question with YOLOv8, determining the option with YOLOv8, and determining the question number with the Permutated Autoregressive Sequence Model. The model accuracy obtained from real data shows the feasibility of the study as a result of the measurements.