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    A Novel Hybrid Optic Disc Detection and Fovea Localization Method Integrating Region-Based Convnet and Mathematical Approach
    (Springer, 2023) Dinc, Baris; Kaya, Yasin
    Optic disc (OD), fovea, and blood vessels are major anatomical structures in a fundus image. In retinal image processing, automated detection of structural parts is crucial in analyzing image patterns and abnormalities caused by eye diseases such as glaucoma, macular edema, and diabetic retinopathy. This paper presents a robust and efficient OD detection and fovea localization method integrating region-based deep convolutional neural networks and a mathematical approach. The proposed model consists of two stages: In the first stage, we generated multiple OD region proposals and then detected the OD based on the boundary box with the highest score using Faster R-CNN. In the second stage, we calculated the localization of the fovea employing a mathematical model considering the coordinates of the predicted OD region. We used four publically available fundus image databases, ORIGA-light, DRIVE, DIARET-DB1, and MESSIDOR, to evaluate our model. The proposed hybrid model was trained with 70% images of the ORIGA-light database and tested on 30% images of ORIGA-light and all images of the other databases. To show the robustness of the model, all databases were divided into two parts, as normal and diseased samples. The presented model achieved a reliable and flexible performance in detecting the OD, with overall IoU results of 88.5, 75.5, 84.4, and 86.8% on ORIGA-light, DRIVE, DIARET-DB1, and MESSIDOR databases, respectively. Moreover, the average fovea localization results in terms of IoU were 58.1, 66.1, 71, and 73.2% on ORIGA-light, DRIVE, DIARET-DB1, and MESSIDOR databases, respectively. The experimental tests demonstrate that the proposed approach achieves promising results for both normal and diseased images.
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    Öğe
    HBDFA: An intelligent nature-inspired computing with high-dimensional data analytics
    (Springer, 2024) Dinc, Baris; Kaya, Yasin
    The rapid development of data science has led to the emergence of high-dimensional datasets in machine learning. The curse of dimensionality is a significant problem caused by high-dimensional data with a small sample size. This paper proposes a novel hybrid binary dragonfly algorithm (HBDFA) in which a distance-based similarity evaluation algorithm is embedded before the dragonfly algorithm (DA) searching behavior to select the most discriminating features. The two-step feature selection mechanism of HBDFA enables the method to explore the feature space reduced by the distance-based similarity evaluation algorithm. The model was evaluated on two datasets. The first dataset contained 200 reports from 4 evenly distributed categories of Daily Mail Online: COVID-19, economy, science, and sports. The second dataset was the publicly available Spam dataset. The proposed model is compared with binary versions of four popular metaheuristic algorithms. The model achieved an accuracy rate of 96.75% by reducing 66.5% of the top 100 features determined on the first dataset. Results on the Spam dataset reveal that HBDFA gives the best classification results with over 95% accuracy. The experimental results show the superiority of HBDFA in searching high-dimensional data, improving classification results, and reducing the number of selected features.