Diabetic retinopathy (DR) is a common complication of diabetes that can lead to progressive vision loss. Regular surveillance with fundal photography, early diagnosis, and prompt intervention are paramount to reducing the incidence of DR-induced vision loss. However, manual interpretation of fundal photographs is subject to human error. In this study, a new method based on horizontal and vertical patch division was proposed for the automated classification of DR images on fundal photographs. The novel sides of this study are given as follows. We proposed a new non-fixed-size patch division model to obtain high classification results and collected a new fundus image dataset. Moreover, two datasets are used to test the model: a newly collected three-class (normal, non-proliferative DR, and proliferative DR) dataset comprising 2355 DR images and the established open-access five-class Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 dataset comprising 3662 images. Two analysis scenarios, Case 1 and Case 2, with three (normal, non-proliferative DR, and proliferative DR) and five classes (normal, mild DR, moderate DR, severe DR, and proliferative DR), respectively, were derived from the APTOS 2019 dataset. These datasets and these cases have been used to demonstrate the general classification performance of our proposal. By applying transfer learning, the last fully connected and global average pooling layers of the DenseNet201 architecture were used to extract deep features from input DR images and each of the eight subdivided horizontal and vertical patches. The most discriminative features are then selected using neighborhood component analysis. These were fed as input to a standard shallow cubic support vector machine for classification. Our new DR dataset obtained 94.06% and 91.55% accuracy values for three-class classification with 80:20 hold-out validation and 10-fold cross-validation, respectively. As can be seen from steps of the proposed model, a new patch-based deep-feature engineering model has been proposed. The proposed deep-feature engineering model is a cognitive model, since it uses efficient methods in each phase. Similar excellent results were seen for three-class classification with the Case 1 dataset. In addition, the model attained 87.43% and 84.90% five-class classification accuracy rates using 80:20 hold-out validation and 10-fold cross-validation, respectively, on the Case 2 dataset, which outperformed prior DR classification studies based on the five-class APTOS 2019 dataset. Our model attained about >2% classification results compared to others. These findings demonstrate the accuracy and robustness of the proposed model for classification of DR images.
Purpose Pseudoexfoliation syndrome (PEX) is an eye disease that develops under the influence of regional population differences, genetic factors, age, and environmental factors and is characterized by visualization of a gray-white fibrogranular substance in the lens anterior capsule and/or pupil margin during anterior segment examination. The underlying biochemical mechanisms of the disease have not yet been fully elucidated. Therefore, this study was designed to show the changes in aqueous humor and blood serum levels of matrix metalloproteinases (decorin and tenascin C), total antioxidants (TAS), and total oxidants (TOS) in both cataract patients who have unilateral PEX material and cataract patients who do not have unilateral PEX material. Methods Biological samples were simultaneously collected from 22 cataract patients who had unilateral pseudoexfoliation (PEX patients) and 22 cataract patients who did not have unilateral pseudoexfoliation (control patients). From the collected biological samples, decorin (DEC) and tenascin C (TN-C) were measured with the enzyme-linked immunosorbent assay (ELISA) method, and TAS and TOS were measured with an autoanalyzer. Results When decorin, tenascin C, and TOS values of PEX patients were compared with those of control patients, there was a statistically significant increase in all three parameters. Conversely, TAS values showed a statistically significant decrease in PEX patients compared to controls. DEC, TN-C, TAS values, and TOS values were significantly higher in aqueous fluid than in blood in both the PEX patient and control groups. Conclusions We suggest that parameters such as DEC, TN-C, TAS, and TOS play a role in the etiopathology of pseudoexfoliation syndrome. Thus, bringing these increased levels of extracellular proteins and TOS and decreased levels of TAS back to within physiological limits can mediate the reorganization of the blood-aqueous fluid barrier and slow the progression of pseudoexfoliation syndrome.
Aim: The aim of this study was to evaluate the central corneal thickness (CCT) and central corneal epithelial thickness (CCET) in patients with Type 2 diabetes mellitus (DM), and the effect of the duration of diabetes, the degree of diabetic retinopathy (DR), and HbA1c level.Method: CCT and CCET values of 72 patients diagnosed with type 2 DM and 72 healthy individuals were measured by anterior segment optical coherence tomography (AS-OCT). The eye tear function was evaluated with the Tear Break-up Time test (TBUT) and the Schirmer test. From the results of fundus examination, the diabetic patients were grouped as those without DR, with non-proliferative DR, and with proliferative DR. The disease duration and the HbA1c levels were recorded.Results: In the diabetic patients, the mean CCT was determined to be thicker (p=0.025), the CCET was thinner (p=0.003), and the TBUT and Schirmer values were lower (p<0.001, p<0.001, respectively). The duration of diabetes and the HbA1c level were not found to have any statistically signi cant effect on these parameters (p>0.05). The presence of retinopathy had no signi cant effect on CCT, TBUT and Schirmer values. The CCET was determined to be thinner in patients with retinopathy (p<0.001). Conclusion:As the corneal epithelial thickness is reduced in patients with advanced diabetic retinopathy, corneal epithelial pathologies can be seen more often. Therefore, early and effective treatment can be started taking into consideration the complications which may develop associated with the corneal epithelium following surgical procedures, especially those applied to the cornea.
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