Yazar "Yusufoglu, Elif" seçeneğine göre listele

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    A Comprehensive CNN Model for Age-Related Macular Degeneration Classification Using OCT: Integrating Inception Modules, SE Blocks, and ConvMixer
    (Mdpi, 2024) Yusufoglu, Elif; Firat, Huseyin; Uzen, Huseyin; Ozcelik, Salih Taha Alperen; Cicek, Ipek Balikci; Sengur, Abdulkadir; Atila, Orhan
    Background/Objectives: Age-related macular degeneration (AMD) is a significant cause of vision loss in older adults, often progressing without early noticeable symptoms. Deep learning (DL) models, particularly convolutional neural networks (CNNs), demonstrate potential in accurately diagnosing and classifying AMD using medical imaging technologies like optical coherence to-mography (OCT) scans. This study introduces a novel CNN-based DL method for AMD diagnosis, aiming to enhance computational efficiency and classification accuracy. Methods: The proposed method (PM) combines modified Inception modules, Depthwise Squeeze-and-Excitation Blocks, and ConvMixer architecture. Its effectiveness was evaluated on two datasets: a private dataset with 2316 images and the public Noor dataset. Key performance metrics, including accuracy, precision, recall, and F1 score, were calculated to assess the method's diagnostic performance. Results: On the private dataset, the PM achieved outstanding performance: 97.98% accuracy, 97.95% precision, 97.77% recall, and 97.86% F1 score. When tested on the public Noor dataset, the method reached 100% across all evaluation metrics, outperforming existing DL approaches. Conclusions: These results highlight the promising role of AI-based systems in AMD diagnosis, of-fering advanced feature extraction capabilities that can potentially enable early detection and in-tervention, ultimately improving patient care and outcomes. While the proposed model demon-strates promising performance on the datasets tested, the study is limited by the size and diversity of the datasets. Future work will focus on external clinical validation to address these limita-tions.
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    Central serous retinopathy classification with deep learning-based multilevel feature extraction from optical coherence tomography images
    (Elsevier Sci Ltd, 2025) Uzen, Huseyin; Firat, Huseyin; Ozcelik, Salih Taha Alperen; Yusufoglu, Elif; Cicek, Ipek Balikci; Sengur, Abdulkadir
    Central Serous Chorioretinopathy (CSCR) is an ocular disease characterized by fluid accumulation under the retina, which can lead to permanent visual impairment if not diagnosed early. This study presents a deep learning-based Convolutional Neural Network (CNN) model designed to automatically diagnose acute and chronic CSCR from Optical Coherence Tomography (OCT) images through multi-level feature extraction. The proposed CNN architecture consists of consecutive layers like a traditional CNN. However, it also extracts various features by creating feature maps at four different levels (F1, F2, F3, F4) for the final feature map. The model processes information using group-wise convolution and Pointwise Convolution Block (PCB) at each level. In this way, each feature group is further processed to obtain more representative features, enabling more independent learning. After the PCB outputs, the 4 feature maps are vectorized and combined, thus creating the final feature map. Finally, classification prediction scores are obtained by applying a fully connected layer and softmax function to this feature map. The experimental study utilized two datasets obtained from Elazig Ophthalmology Polyclinic. The dataset includes 3860 OCT images from 488 individuals, with images categorized into acute CSCR, chronic CSCR, wet AMD, dry AMD, and healthy controls. Our proposed method achieves an increase in accuracy of 0.77%, attaining 96.40% compared to the highest previous accuracy of 95.73% by ResNet101. Precision is enhanced by 0.95%, reaching 95.16% over ResNet101 ' s 94.21%. The sensitivity (recall) is improved by 0.90%, achieving 95.65% versus ResNet101 ' s 94.75%. Additionally, the F1 score is increased by 0.93%, attaining 95.38% compared to ResNet101 ' s 94.45%. These results illustrate the effectiveness of our method, offering more precise and reliable diagnostic capabilities in OCT image classification. In conclusion, this study demonstrates the potential of artificial intelligence-supported diagnostic tools in the analysis of OCT images and contributes significantly to the development of early diagnosis and treatment strategies.
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    Evaluation of central macular thickness after penetrating keratoplasty
    (2018) Can, Nagehan; Guler, Mete; Yusufoglu, Elif; Celik, Fatih; Gul, Fatih Cem; Ozsoy, Ercan
    Aim: To evaluate the changes in central macular thickness after penetrating keratoplasty. Material and Methods: A total of 24 eyes of 24 patients who had undergone penetrating keratoplasty were included in the study. This study was performed retrospectively by reviewing the charts of the patients. Postoperative 1st week, 1st month, 3rd month, 6th month and 12th month mean total macular volume, central macular thickness, parafoveal area and perifoveal area thickness and retinal nevre fiber layer (RNFL) thickness results obtained with optic coherence tomography were compared. ANOVA test was used for statistical analysis. Results: The postoperative 1st week, 1st month, 3rd month, 6th month and 12th month mean total macular volume measurements were 7.03±0.2 mm³, 7.05±0.4 mm³, 7.0±0.6 mm³, 7.02±0.5 mm³ and 6.12±0.6 mm³, respectively. Mean central macular thickness measurements were 227.6±4.6 μm, 228.7±5.5 μm, 227.2±4.6 μm, 227.5±7.1 μm, 226.3±5.1μm respectively; mean parafoveal area thickness measurements were 290.2±3.7 μm, 289.9±7.8 μm, 288.7±6.3 μm, 288.8±4.7 μm, 288.6±8.3 μm respectively, mean perifoveal area thickness measurements were 261.1±4.2 μm, 261.4±1.9 μm, 260.4±3.6 μm, 259.8±2.7 μm, 259.3±4.7 μm respectively, and mean RNFL thickness measurements were 106.54±11.28 μm, 107.28±8.75 μm, 107.45±13.64 μm, 105.62±9.27 μm, 105.16±12.74 μm; respectively. Conclusion: No significant change was seen in macular thickness after penetrating keratoplasty. Although the macular thickness increases in the early postoperative stage, it decreases in time.