Yazar "Firildak, Kazim" seçeneğine göre listele

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    Cat Swarm Based Shadow Detection Method
    (Ieee, 2018) Firildak, Kazim; Karaduman, Mucahit; Talu, Muhammed Fatih; Yeroglu, Celaleddin
    Innovations brought by developing technology are used in different forms in security area. Those technologies on imaging are usually involved in monitoring, tracking and detecting. When these processes are performed, the shadow of the object can prevent detection and monitoring. Therefore, in this study, a method has been proposed to determine the shadows of the objects and to distinguish them from the original image, and to determine the real image of the object. Previous work on this area has been done using classic shadow detection methods. In order to overcome their disadvantages during the detection period, this study uses cat swarm optimization, which can be applied to many problems. With this method, the shadows belonging to the object are detected, and the objects are determined by separating the shadows from the image. Later, if desired, follow-up of the object will also be achieved. It is shown that the suggested cat swarm optimization algorithm provides an effective result in shadow detection and give better results with shadow detection rate over compared algorithms.
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    A Hybrid Capsule Network for Pneumonia Detection Using Image Augmentation Based on Generative Adversarial Network
    (Int Information & Engineering Technology Assoc, 2021) Firildak, Kazim; Talu, Muhammed Fatih
    Pneumonia, featured by inflammation of the air sacs in one or both lungs, is usually detected by examining chest X-ray images. This paper probes into the classification models that can distinguish between normal and pneumonia images. As is known, trained networks like AlexNet and GoogleNet are deep network architectures, which are widely adopted to solve many classification problems. They have been adapted to the target datasets, and employed to classify new data generated through transfer learning. However, the classical architectures are not accurate enough for the diagnosis of pneumonia. Therefore, this paper designs a capsule network with high discrimination capability, and trains the network on Kaggle' s online pneumonia dataset, which contains chest X-ray images of many adults and children. The original dataset consists of 1,583 normal images, and 4,273 pneumonia images. Then, two data augmentation approaches were applied to the dataset, and their effects on classification accuracy were compared in details. The model parameters were optimized through five different experiments. The results show that the highest classification accuracy (93.91% even on small images) was achieved by the capsule network, coupled with data augmentation by generative adversarial network (GAN), using optimized parameters. This network outperformed the classical strategies.
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    Negative Selection Based Shadow Detection Algorithm
    (Ieee, 2015) Firildak, Kazim; Karakose, Mehmet
    Shadow regions are detected as objects at image segmentation, object detection and tracking applications. So, it affects negatively the accuracy and performance of algorithms. In this study, artificial immune system-based negative selection algorithm (YBSG) is proposed in order to determine shadow region. This algorithm obtains fast and effective solution to detect nonlinear change shadow region on the video scenes. This algorithm obtains us to increase in 5%-20% shadow detecting performance and 5%-10% execution time with effective method proposed to detect the shadows of different video scenes in literature.
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    A Shadow Detection Approach Based on Fuzzy Logic Using Images Obtained from PV Array
    (Ieee, 2015) Karakose, Mehmet; Firildak, Kazim
    Shadows on PV arrays influence the energy production performance negatively. There are many methods in the literature related to the detection of these shadows and reconfiguration of arrays. The methods proposed in the literature generally aim at reconfiguration of arrays and detecting shadow regions by using current (I), voltage (V) and power (P) information. In the process of reconfiguration it is quite difficult to measure to use P, V, I information. In this paper, in order to use in the reconfiguration processes for the detection of the shadow region on PV arrays, a new fuzzy logic based computer vision method is presented. The proposed method, firstly, performs object detection with background subtraction. Then, detects the edge of the object regions. Finally, it detects shadow regions with the help of fuzzy logic decision making system. Experimental results are provides effective performance for detecting of shaded areas on PV arrays.
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    Supervised Constructive Learning-Based Model for Identifying Colorectal Cancer Tissue Types From Histopathological Images
    (Wiley, 2025) Firildak, Kazim; Celik, Gaffari; Talu, Muhammed Fatih
    Colorectal cancer is the disease with the second highest mortality rate among cancer types. The survival rate is increased with early diagnosis and treatment of this disease. In this study, a supervised constructive learning based model is proposed for the detection of colorectal cancer using datasets containing hematoxylin and eosin stained colon histopathological images. The datasets used include multi-class datasets (Kather-5K, CRC-7K, NCT-100K) and binary class datasets (Kather MSI and MHIST). The proposed model consists of an encoder (ReFeatureBlock (RFB), depthwise convolution (DWC), and global average pooling (GAP)), a projection head, and fully connected classification networks. With these networks, it is possible to obtain important features, reduce the computational cost, minimize noise sensitivity, and prevent poor margin possibilities. Additionally, the Grad-CAM method was used to ensure transparency and explainability of the model's decision-making processes. In multiple classification experiments, in applications performed by combining Kather-5K, CRC-7K, and NCT-100K datasets, the proposed model achieved the highest performance with 99.21% accuracy, 99.19% precision, 99.19% recall, 99.19% F1-score, 99.92% specificity, and 99.56% AUC values, respectively. In addition, in tests performed on individual datasets, high performances such as 99.10% accuracy for Kather-5K, 99.76% accuracy for CRC-7K, and 99.19% accuracy for NCT-100K were achieved. In binary classification experiments with the MHIST dataset, the proposed model showed the highest success with 99.52% accuracy, 99.30% precision, 99.49% recall, 99.40% F1-score, 99.49% specificity, and 99.49% AUC, respectively. Moreover, the proposed model is compared with state-of-the-art techniques in the literature in the classification of colorectal cancer tissues, and the results are discussed. The findings show that the proposed model provides higher classification success in statistical metrics. The codes of the proposed model are publicly available at .