Yazar "Ozcelik, Salih Taha Alperen" 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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    Channel-boosted multi-scale R-CNN for accurate and real-time ship and land detection in complex SAR scenes
    (Elsevier Sci Ltd, 2026) Hanbay, Kazim; Ozcelik, Salih Taha Alperen; Altin, Mustafa; Uzen, Huseyin
    Accurate ship detection in Synthetic Aperture Radar (SAR) imagery is crucial for maritime surveillance but remains challenging due to small target sizes, speckle noise, and complex sea-surface backgrounds. While most existing methods focus exclusively on identifying ships, our approach also achieves reliable detection of land areas, providing an additional contribution to the literature. This study introduces CBM-RCNN (Channel-Boosted Multi-Scale R-CNN), a novel deep learning architecture that integrates Convolutional Block Attention Modules (CBAM) and a Bidirectional Feature Pyramid Network (BiFPN) on a ResNet50 backbone. CBAM enhances both spatial and channel-level feature representation, enabling reliable detection of small vessels, while BiFPN fuses multi-scale features bidirectionally, improving accuracy across vessels of different sizes and positions. CBM-RCNN was evaluated against standard Faster R-CNN and YOLOv8 models across diverse maritime scenes, including simple, densely populated, and visually complex scenarios. The model demonstrated superior detection accuracy, balanced class-specific performance, and strong generalization. It effectively resolves overlapping vessels, distinguishes ships from coastal structures, and maintains robustness under challenging SAR-specific noise conditions. Importantly, it achieves inference speeds suitable for near-real-time applications, highlighting practical applicability. By combining attention-driven refinement with multi-scale feature aggregation, CBM-RCNN addresses limitations of prior methods, particularly in small object recognition, complex scene generalization, and simultaneous land detection. This architecture provides a robust framework for automated maritime monitoring and offers a foundation for future improvements in large-scale SAR-based ship detection and environmental surveillance.
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    Deep Learning-Assisted Detection and Classification of Thymoma Tumors in CT Scans
    (Mdpi, 2025) Kilic, Murat; Biyikli, Merve; Ozcelik, Salih Taha Alperen; Uzen, Huseyin; Firat, Huseyin
    Background/Objectives: Thymoma is a rare epithelial neoplasm originating from the thymus gland, and its accurate detection and classification using computed tomography (CT) images remain diagnostically challenging due to subtle morphological similarities with other mediastinal pathologies. This study presents a deep learning (DL)-based model designed to improve diagnostic accuracy for both thymoma detection and subtype classification (benign vs. malignant). Methods: The proposed approach integrates a pre-trained VGG16 network for efficient feature extraction-capitalizing on its capacity to capture hierarchical spatial features-and an MLP-Mixer-based feature enhancement module, which effectively models both local and global feature dependencies without relying on conventional convolutional mechanisms. Additionally, customized preprocessing and post-processing methods are employed to enhance image quality and suppress redundant data. The model's performance was evaluated on two classification tasks: distinguishing thymoma from healthy cases and discriminating between benign and malignant thymoma. Comparative analysis was conducted against state-of-the-art DL models including ResNet50, ResNet34, SEResNeXt50, InceptionResNetV2, MobileNetV2, VGG16, InceptionV3, and DenseNet121 using metrics such as F1 score, accuracy, recall, and precision. Results: The model proposed in this study obtained its best performance in thymoma vs. healthy classification, with an accuracy of 97.15% and F1 score of 80.99%. In the benign vs. malignant task, it attained an accuracy of 79.20% and an F1 score of 78.51%, outperforming all baseline methods. Conclusions: The integration of VGG16's robust spatial feature extraction and the MLP-Mixer's effective feature mixing demonstrates superior and balanced performance, highlighting the model's potential for clinical decision support in thymoma diagnosis.