Yazar "Gursoy, Ercan" seçeneğine göre listele

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    A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection
    (Springer, 2023) Kaya, Yasin; Gursoy, Ercan
    COVID-19 is a virus that causes upper respiratory tract and lung infections. The number of cases and deaths increased daily during the pandemic. Once it is vital to diagnose such a disease in a timely manner, the researchers have focused on computer-aided diagnosis systems. Chest X-rays have helped monitor various lung diseases consisting COVID-19. In this study, we proposed a deep transfer learning approach with novel fine-tuning mechanisms to classify COVID-19 from chest X-ray images. We presented one classical and two new fine-tuning mechanisms to increase the model's performance. Two publicly available databases were combined and used for the study, which included 3616 COVID-19 and 1576 normal (healthy) and 4265 pneumonia X-ray images. The models achieved average accuracy rates of 95.62%, 96.10%, and 97.61%, respectively, for 3-class cases with fivefold cross-validation. Numerical results show that the third model reduced 81.92% of the total fine-tuning operations and achieved better results. The proposed approach is quite efficient compared with other state-of-the-art methods of detecting COVID-19.
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    A novel multi-head CNN design to identify plant diseases using the fusion of RGB images
    (Elsevier, 2023) Kaya, Yasin; Gursoy, Ercan
    Plant diseases and insect pests cause a significant threat to agricultural production. Early detection and diagnosis of these diseases are critical and can reduce economic losses. The recent development of deep learning (DL) benefits various fields, such as image processing, remote sensing, medical diagnosis, and agriculture. This work proposed a novel approach based on DL for plant disease detection by fusing RGB and segmented images. A multi-headed DenseNet-based architecture was developed, considering two images as input. We evaluated the model on a public dataset, PlantVillage, consisting of 54183 images with 38 classes. The fivefold cross-validation technique achieved an average accuracy, recall, precision, and f1-score of 98.17%, 98.17%, 98.16%, and 98.12%, respectively. The proposed approach can distinguish various plant diseases with different characteristics by image fusion. The high success rate with low standard deviation proves the robustness of the model, and the model can be integrated into plant disease detection and early warning system.
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    A review of deep learning architectures for plant disease detection
    (Tubitak Scientific & Technological Research Council Turkey, 2025) Kaya, Yasin; Gursoy, Ercan
    Background/aim: The rapid advancement of deep learning (DL) has revolutionized plant disease detection by enabling highly accurate, image-based diagnostic solutions. This review provides a comprehensive synthesis of DL-based methodologies for plant disease detection, systematically structured around the key stages of the modeling pipeline, encompassing data acquisition, preprocessing, augmentation, classification, detection, segmentation, and deployment. Materials and methods: The review focuses on evaluating convolutional neural network (CNN) architectures such as VGG, ResNet, EfficientNet, and DenseNet across diverse experimental contexts. Classification strategies are categorized according to their integration of visualization techniques (e.g., saliency maps, Grad-CAM) to enhance model interpretability, emphasizing the pivotal role of explainable artificial intelligence (XAI) in plant pathology. Object detection models are systematically examined within both one-stage (YOLO, SSD) and two-stage (Faster R-CNN) paradigms. Furthermore, critical challenges-such as environmental variability, data imbalance, and computational constraints-along with potential solutions including transfer learning, synthetic data generation using generative adversarial networks (GANs) and diffusion models, and edge computing for real-time deployment, are comprehensively discussed. Results: This review summarizes best practices for dataset selection and model optimization for mobile platforms, emphasizing their role in improving the efficiency and accuracy of plant disease detection systems. Conclusion: Deep learning-based methods show strong potential to enhance precision and resilience in real-world plant disease detection and monitoring.
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    An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works
    (Springer, 2023) Gursoy, Ercan; Kaya, Yasin
    The World Health Organization (WHO) declared a pandemic in response to the coronavirus COVID-19 in 2020, which resulted in numerous deaths worldwide. Although the disease appears to have lost its impact, millions of people have been affected by this virus, and new infections still occur. Identifying COVID-19 requires a reverse transcription-polymerase chain reaction test (RT-PCR) or analysis of medical data. Due to the high cost and time required to scan and analyze medical data, researchers are focusing on using automated computer-aided methods. This review examines the applications of deep learning (DL) and machine learning (ML) in detecting COVID-19 using medical data such as CT scans, X-rays, cough sounds, MRIs, ultrasound, and clinical markers. First, the data preprocessing, the features used, and the current COVID-19 detection methods are divided into two subsections, and the studies are discussed. Second, the reported publicly available datasets, their characteristics, and the potential comparison materials mentioned in the literature are presented. Third, a comprehensive comparison is made by contrasting the similar and different aspects of the studies. Finally, the results, gaps, and limitations are summarized to stimulate the improvement of COVID-19 detection methods, and the study concludes by listing some future research directions for COVID-19 classification.
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    Multi-source deep feature fusion for medical image analysis
    (Springer, 2025) Gursoy, Ercan; Kaya, Yasin
    In image fusion, several images are combined into one image that contains information from all input images. In medical image analysis, image fusion can help to improve the accuracy of diagnosis and treatment planning. One approach to image fusion is the saliency map, where an algorithm highlights the most informative regions of the image and then combines these regions into a single image. This method can be particularly useful in medical image analysis, where certain areas of an image may be especially critical. This study proposes a novel model for multi-head medical image analysis based on ResNet using the fusion of saliency maps and RGB images as input from medical images. The image fusion generated by saliency maps contains more visible features. The saliency maps generated with the pre-trained model also contain background information. A combined dataset from two publicly available sources containing three classes, healthy, COVID-19, and pneumonia X-ray images, was used to evaluate the proposed model. The proposed multi-head CNN model improves the average classification accuracy from 94.68 to 96.72% with five-fold cross-validation. This approach could be implemented in an end-to-end computer-aided diagnosis system to shorten the evaluation time.
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    Multi-source deep feature fusion for medical image analysis (vol 36, 4, 2024)
    (Springer, 2025) Gursoy, Ercan; Kaya, Yasin