Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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  • Conference Object
    Semantic Guided Autoregressive Diffusion Based Data Augmentation Using Visual Instructions
    (Institute of Electrical and Electronics Engineers Inc., 2025) Yavuzcan, Ege; Kus, Omer; Gumus, Abdurrahman
    Recent breakthroughs in generative image models, especially those based on diffusion techniques, have radically transformed the landscape of text-guided image synthesis by delivering exceptional fidelity and detailed semantic control. In this study, we present an iterative editing framework that harnesses the inherent strengths of these generative models to progressively refine images with precision. Our approach begins by generating diverse textual descriptions from an initial image, from which the most effective prompt is selected to drive further refinement through a fine-tuned Stable Diffusion process. This pipeline, as detailed in our flow diagram, orchestrates a series of controlled image modifications that preserve the original context while accommodating deliberate stylistic and semantic adjustments. By cycling the augmented output back into the system, our method achieves a harmonious balance between innovation and consistency, paving the way for highquality, context-aware visual transformations. This dynamic, auto-regressive strategy underscores the transformative potential of modern image generation models for applications that require detailed, controlled creative expression. The code is available on Github. © 2025 Elsevier B.V., All rights reserved.
  • Conference Object
    Iterative Semantic Refinement: A Vision Language Model-Driven Approach to Auto-Regressive Image Editing
    (Institute of Electrical and Electronics Engineers Inc., 2025) Yavuzcan, Ege; Kus, Omer; Gumus, Abdurrahman
    Recent advancements in Visual Language Models (VLMs) have significantly improved text-to-image generation by enabling more nuanced and semantically rich textual prompts, highlighting the transformative impact of these models on image synthesis. In this work, we leverage these robust capabilities to develop an auto-regressive editing framework that systematically refines images through careful, step-by-step modifications. Our method concisely balances subtle adjustments with meaningful semantic shifts, ensuring that each editing stage preserves the core context while introducing precise variations. By integrating improvements from controllable image editing models, we enhance the precision and stability of our edits and demonstrate the effectiveness of our approach in maintaining visual coherence. This integration results in a powerful strategy for producing diverse, high-quality outputs that align with finely tuned semantic goals. Centered on the strength of VLMs, this framework opens up a new paradigm for image synthesis, offering a blend of creative flexibility and consistent contextual fidelity that holds promise for a variety of applications requiring intricate and controlled visual transformations. © 2025 Elsevier B.V., All rights reserved.
  • Article
    Vision Transformers-Based Deep Feature Generation Framework for Hydatid Cyst Classification in Computed Tomography Images
    (Springer, 2025) Sagik, Metin; Gumus, Abdurrahman
    Hydatid cysts, caused by Echinococcus granulosus, form progressively enlarging fluid-filled cysts in organs like the liver and lungs, posing significant public health risks through severe complications or death. This study presents a novel deep feature generation framework utilizing vision transformer models (ViT-DFG) to enhance the classification accuracy of hydatid cyst types. The proposed framework consists of four phases: image preprocessing, feature extraction using vision transformer models, feature selection through iterative neighborhood component analysis, and classification, where the performance of the ViT-DFG model was evaluated and compared across different classifiers such as k-nearest neighbor and multi-layer perceptron (MLP). Both methods were evaluated independently to assess classification performance from different approaches. The dataset, comprising five cyst types, was analyzed for both five-class and three-class classification by grouping the cyst types into active, transition, and inactive categories. Experimental results showed that the proposed VIT-DFG method achieves higher accuracy than existing methods. Specifically, the ViT-DFG framework attained an overall classification accuracy of 98.10% for the three-class and 95.12% for the five-class classifications using 5-fold cross-validation. Statistical analysis through one-way analysis of variance (ANOVA), conducted to evaluate significant differences between models, confirmed significant differences between the proposed framework and individual vision transformer models (p<0.05\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p < 0.05$$\end{document}). These results highlight the effectiveness of combining multiple vision transformer architectures with advanced feature selection techniques in improving classification performance. The findings underscore the ViT-DFG framework's potential to advance medical image analysis, particularly in hydatid cyst classification, while offering clinical promise through automated diagnostics and improved decision-making.
  • Conference Object
    Development of Low-Cost Portable Blood Vessel Imaging System
    (IEEE, 2021) Altay, Ayse; Gumus, Abdurrahman
    As an alternative to high-cost near-infrared (NIR) vascular imaging devices in the market [1], a microcomputerbased, real-time, low-cost, non-contact and safe vascular imaging system has been developed. The higher absorption coefficient of blood from skin and fat, as well as the differences in oxy and deoxyhemoglobin spectra in blood, were helpful factors in the use of the NIR region during the acquisition of vessel images. A device, which uses NIR LED light operated at 850 nm, was designed using optical and electronic components. Image analysis were performed using OpenCV, which is an open-source software library, and data visualization libraries. Tests were carried out to optimize the best imaging conditions for the device. In this study, a portable device design with improved vessel image quality is presented which could potentially be used to assist the health professionals to investigate the abnormalities in the superficial vascular structures at different times during patients' treatments.