Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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

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Author: search.filters.author.Gumus, AbdurrahmanPublisher: search.filters.publisher.Springer

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  • 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.
  • Article
    Citation - WoS: 2
    Vis-Assist: Computer Vision and Haptic Feedback-Based Wearable Assistive Device for Visually Impaired
    (Springer, 2025) Dede, Ibrahim; Gumus, Abdurrahman
    Visual impairment affects millions of people worldwide, posing significant challenges in their daily lives and personal safety. While assistive technologies, both wearable and non-wearable, can help mitigate these challenges, wearable devices offer the advantage of hands-free operation. In this context, we present Vis-Assist, a novel wearable visual assistive device capable of detecting and classifying objects, measuring their distances, and providing real-time haptic feedback through a vibration motor array, all using an integrated low-cost computational unit without the need for external servers. Our study distinguishes itself by utilizing haptic feedback to convey object information, allowing visually impaired individuals to discern between 19 different object classes following a brief training period. Haptic feedback offers an alternative to audio that doesn't block hearing and can be used alongside it, serving as a complementary solution. The performance of the developed wearable device was evaluated through two types of experiments with four participants. The results demonstrate that users can identify the location of objects and thereby prevent collisions with obstacles. The experiments conducted demonstrate that users, on average, can locate a predefined object, such as a chair, within a 40 m2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {m}<^>{2}$$\end{document} vacant space in under 94 seconds. Furthermore, users exhibit proficiency in finding objects while navigating around obstacles in the same environment, achieving this task in less than 121 seconds on average. The system developed here has high potential to help the self-navigation of visually impaired people and make their daily lives easier. To facilitate further research in this field, the complete source code for this study has been made publicly available on GitHub.