WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Article Citation - WoS: 4Citation - Scopus: 5Diffusion-Based Data Augmentation Methodology for Improved Performance in Ocular Disease Diagnosis Using Retinography Images(Springer Heidelberg, 2024) Aktas, Burak; Ates, Doga Deniz; Duzyel, Okan; Gumus, AbdurrahmanDeep learning models, integral components of contemporary technological landscapes, exhibit enhanced learning capabilities with larger datasets. Traditional data augmentation techniques, while effective in generating new data, have limitations, especially in fields like ocular disease diagnosis. In response, alternative augmentation approaches, including the utilization of generative AI, have emerged. In our study, we employed a diffusion-based model (Stable Diffusion) to synthesize data by faithfully recreating crucial vascular structures in the retina, vital for detecting eye diseases by using the Ocular Disease Intelligent Recognition dataset. Our goal was to augment retinography images for ocular disease diagnosis using diffusion-based models, optimizing the outputs of the fine-tuned Stable Diffusion model, and ensuring the generated data closely resembles real-world scenarios. This strategic approach resulted in improved performance in classification models and augmentation outperformed traditional methods, exhibiting high precision rates ranging from 85% to 76.2% and recall values of 86%, and 75% for 5 classes. Beyond performance enhancement, we demonstrated that the inclusion of synthetic data, coupled with data reduction using the t-SNE method, effectively addressed dataset imbalance. As a result of synthetic data addition, notable increases of 3.4% in the precision metric and 12.8% in the recall metric were observed in the 7-class case. Strategically synthesizing data addressed underrepresented classes, creating a balanced dataset for comprehensive model learning. Surpassing performance improvements, this approach underscores synthetic data's ability to overcome the limitations of traditional methods, particularly in sensitive medical domains like ocular disease diagnosis, ensuring accurate classification. The codes of the study will be shared on GitHub in a way that benefits everyone interested: https://github.com/miralab-ai/generative-data-augmentation.Article Citation - Scopus: 1Curve Description by Histograms of Tangent Directions(Institution of Engineering and Technology, 2019) Köksal, Ali; Özuysal, MustafaThe authors propose a novel approach for the description of objects based on contours in their images using real-valued feature vectors. The approach is particularly suitable when objects of interest have high contrast and texture-free images or when the texture variations are high so textural cues are nuisance factors for classification. The proposed descriptor is suitable for nearest neighbour classification still popular in embedded vision applications when the power considerations outweigh the performance requirements. They describe object outlines purely based on the histograms of contour tangent directions mimicking many of the design heuristics of texture-based descriptors such as scale-invariant feature transform (SIFT). However, unlike SIFT and its variants, the proposed approach is directly designed to work with contour data and it is robust to variations inside and outside the object outline as well as the sampling of the contour itself. They show that relying on tangent direction estimation as opposed to gradient computation yields a more robust description and higher nearest neighbour classification rates in a variety of classification problems.