# Uğur Şevik

**Type:** Researchers  
**Canonical URL:** https://scholariq.org/researchers/ugur-sevik/

## Facts

| Field | Value |
| --- | --- |
| Citations | 431 |
| Field | Retinal Imaging and Analysis |
| h-index | 6 |
| i10-index | 6 |
| Last Known Institution | Karadeniz Technical University |
| OpenAlex ID | https://openalex.org/A5077288199 |
| ORCID iD | 0000-0002-2056-9988 |
| Works | 15 |

## Researcher papers

- [Identification of suitable fundus images using automated quality assessment methods](https://scholariq.org/papers/identification-of-suitable-fundus-images-using-automated-quality-assessment/)
- [Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images](https://scholariq.org/papers/simple-methods-for-segmentation-and-measurement-of-diabetic-retinopathy-lesions/)
- [Automatic segmentation of age-related macular degeneration in retinal fundus images](https://scholariq.org/papers/automatic-segmentation-of-age-related-macular-degeneration-in-retinal-fundus/)
- [A Statistical Segmentation Method for Measuring Age-Related Macular Degeneration in Retinal Fundus Images](https://scholariq.org/papers/a-statistical-segmentation-method-for-measuring-age-related-macular-degeneration/)
- [Automatic classification of skin burn colour images using texture‐based feature extraction](https://scholariq.org/papers/automatic-classification-of-skin-burn-colour-images-using-texture-based-feature/)
- [An automatic diagnosis method for the knee meniscus tears in MR images](https://scholariq.org/papers/an-automatic-diagnosis-method-for-the-knee-meniscus-tears-in-mr-images/)
- [Detection of Dental Anomalies in Digital Panoramic Images Using YOLO: A Next Generation Approach Based on Single Stage Detection Models](https://scholariq.org/papers/detection-of-dental-anomalies-in-digital-panoramic-images-using-yolo-a-next/)
- [Implementation of Digitalization In Food Industry](https://scholariq.org/papers/implementation-of-digitalization-in-food-industry/)
- [Automated Multi-Class Classification of Retinal Pathologies: A Deep Learning Approach to Unified Ophthalmic Screening](https://scholariq.org/papers/automated-multi-class-classification-of-retinal-pathologies-a-deep-learning/)
- [A computationally efficient hybrid framework combining deep feature extraction and gradient boosting for early diagnosis of Olive leaf diseases](https://scholariq.org/papers/a-computationally-efficient-hybrid-framework-combining-deep-feature-extraction/)

## Researcher topics

- [Retinal Imaging and Analysis](https://scholariq.org/topics/retinal-imaging-and-analysis/)
- [Digital Imaging for Blood Diseases](https://scholariq.org/topics/digital-imaging-for-blood-diseases/)
- [Glaucoma and retinal disorders](https://scholariq.org/topics/glaucoma-and-retinal-disorders/)
- [Smart Agriculture and AI](https://scholariq.org/topics/smart-agriculture-and-ai/)
- [Remote Sensing in Agriculture](https://scholariq.org/topics/remote-sensing-in-agriculture/)

## Researcher university

- [Karadeniz Technical University](https://scholariq.org/institutions/karadeniz-technical-university/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.