Yazar "Tandirovic Gursel, Amira" seçeneğine göre listele

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    Mam-Incept-Net: a novel inception model for precise interpretation of mammography images
    (PeerJ Inc, 2025) Tandirovic Gursel, Amira; Kaya, Yasin
    Early diagnosis of breast cancer through periodic screening is a vital ally in the fight for survival. Mammography, recognized as one of the most widely used and cost-effective tools for detecting early signs of asymmetry, calcification, masses, and architectural distortion in breast tissue, plays a significant role in nearly all screening scenarios. However, the interpretation and scoring of mammograms is a complex multi-parameter process that frequently leads to false-positive and false-negative results. This article introduces a new deep-learning-based model that classifies mammograms according to the Breast Imaging Reporting and Data System (BI-RADS) assessment categories. The model is trained on a private dataset, intentionally excluding no BI-RADS categories. A novel deep neural network architecture is employed to more accurately classify breasts, including their boundaries, as regions of interest (ROIs). The ConvNeXt architecture serves as a feature extractor for lower-level features, which are then combined with the layers of a randomly initialized naive inception module to capture higher-level features. Diagnosis is achieved through three experimental tests, yielding accuracy rates ranging from 82.08% to 86.27%. These promising accuracy levels, in comparison to previous studies, can be attributed to a more comprehensive approach to addressing BI-RADS scoring challenges. In addition to pursuing further enhancements in accuracy, future research should consider integrating prior radiology reports to create a more realistic end-to-end computer-aided detection system.
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    Theoretical analysis of doping management and its effects on power scaling
    (Tubitak Scientific & Technological Research Council Turkey, 2016) Tandirovic Gursel, Amira; Elahi, Parviz; Ilday, Fatih Omer; Ozyazici, Mustafa Sadettin
    Thermal load and nonlinear effects are two contrary phenomena that make up important drawbacks in rapid progress of high-power fiber lasers. To minimize the thermal load, which limits the average power, doping concentration should be decreased, which brings about increasing length of the fiber. In contrast, the presence of nonlinear effects and their management demand the use of high-doped, shorter fibers in order to maximize the peak power. Management on doping of gain fiber and obtaining a specific doping profile function along the short gain fiber is a proposed solution for prevention of the exchange between thermal load and nonlinear effects. The study shows two different approaches for keeping the temperature levels down in addition to obtaining power scaling profiles.