Yazar "Firat, Hueseyin" seçeneğine göre listele

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    ConvNext Mixer-Based Encoder Decoder Method for Nuclei Segmentation in Histopathology Images
    (Wiley, 2024) Firat, Hueseyin; Uzen, Hueseyin; Hanbay, Davut; Sengur, Abdulkadir
    Histopathology, vital in diagnosing medical conditions, especially in cancer research, relies on analyzing histopathology images (HIs). Nuclei segmentation, a key task, involves precisely identifying cell nuclei boundaries. Manual segmentation by pathologists is time-consuming, prompting the need for robust automated methods. Challenges in segmentation arise from HI complexities, necessitating advanced techniques. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have transformed nuclei segmentation. This study emphasizes feature extraction, introducing the ConvNext Mixer-based Encoder-Decoder (CNM-ED) model. Unlike traditional CNN based models, the proposed CNM-ED model enables the extraction of spatial and long context features to address the inherent complexities of histopathology images. This method leverages a multi-path strategy using a traditional CNN architecture as well as different paths focused on obtaining customized long context features using the ConvNext Mixer block structure that combines ConvMixer and ConvNext blocks. The fusion of these diverse features in the final segmentation output enables improved accuracy and performance, surpassing existing state-of-the-art segmentation models. Moreover, our multi-level feature extraction strategy is more effective than models using self-attention mechanisms such as SwinUnet and TransUnet, which have been frequently used in recent years. Experimental studies were conducted using five different datasets (TNBC, MoNuSeg, CoNSeP, CPM17, and CryoNuSeg) to analyze the performance of the proposed CNM-ED model. Comparisons were made with various CNN based models in the literature using evaluation metrics such as accuracy, AJI, macro F1 score, macro intersection over union, macro precision, and macro recall. It was observed that the proposed CNM-ED model achieved highly successful results across all metrics. Through comparisons with state-art-of models from the literature, the proposed CNM-ED model stands out as a promising advancement in nuclei segmentation, addressing the intricacies of histopathological images. The model demonstrates enhanced diagnostic capabilities and holds the potential for significant progress in medical research.
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    Multiscale Feature Fusion for Hyperspectral Image Classification Using Hybrid 3D-2D Depthwise Separable Convolution Networks
    (Int Information & Engineering Technology Assoc, 2023) Firat, Hueseyin; Cig, Harun; Guellueoglu, Mehmet Tahir; Asker, Mehmet Emin; Hanbay, Davut
    Hyperspectral remote sensing images (HRSI) comprise three-dimensional image cubes, containing a single spectral dimension alongside two spatial dimensions. HRSI are presently among the foremost essential datasets for Earth observation. The task of HRSI classification is intricate due to the influence of spectral mixing, leading to notable variability within classes and resemblances across classes. Consequently, the field of HRSI classification has garnered significant research attention in recent times. Convolutional Neural Networks (CNNs) are harnessed to address these issues, enabling both feature extraction and classification. This study introduces a novel approach for HRSI classification called the hybrid 3D-2D depthwise separable convolution network (Hybrid DSCNet), which leverages multiscale feature integration. Within the Hybrid DSCNet, diverse kernel sizes contribute to an enriched feature extraction process from HRSI. The conventional 3D-2D CNN, while effective, comes with a computational load. Instead of using the standard 3D-2D CNN, this study adopts the 3D-2D DSC architecture. This approach partitions the conventional convolution into two components: pointwise and depthwise convolution, yielding a substantial reduction in trainable parameters and computational complexity. To evaluate the proposed method, the Indian Pines dataset along with WHU-Hi subdatasets (LongKou-LK, HanChuan-HC, and HongHu-HH) were employed. Employing a 5% training sample, impressive overall accuracy scores were achieved: 94.51%, 99.78%, 97.06%, and 97.27% for Indian Pines, WHU-LK, WHU-HC, and WHU-HH, respectively. Comparative analysis of the proposed approach with cutting-edge techniques within the literature reveals its superior performance across the four HRSI datasets. Notably, the Hybrid DSCNet attains enhanced classification accuracy while maintaining lower computational overhead.
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    Spatial-spectral classification of hyperspectral remote sensing images using 3D CNN based LeNet-5 architecture
    (Elsevier, 2022) Firat, Hueseyin; Asker, Mehmet Emin; Bayindir, Mehmet Ilyas; Hanbay, Davut
    Hyperspectral remote sensing image (HRSI) analysis are commonly used in a wide variety of remote sensing applications such as land cover analysis, military surveillance, object detection and precision agriculture. Deep learning is seen as an effective method to improve HRSI classification. In particular, convolutional neural net-works (CNNs) are increasingly used in this field. The high dimensionality of the HRSIs increases the computa-tional complexity. Thus, most of studies apply dimension reduction as preprocessing. Another problem in HRSI classification is that spatial-spectral features must be considered in order to obtain accurate results. Because, HRSI classification results are highly dependent on spatiospectral information. The aim of this paper is to build a 3D CNN-based LeNet-5 method for HRSI classification. Principal component analysis (PCA) is used as the pre-processing step for optimum spectral band extraction. 3D CNN is used to simultaneously extract spatial -spectral features in HRSIs. LeNet-5 architecture has a simple and straightforward architecture. At the same time, the number of trainable parameters is very low. With the use of the LeNet-5 architecture, the number of trainable parameters of the proposed method is considerably reduced. This is one of the most important features that distinguish the proposed method from other deep learning methods. The proposed method is tested with Indian pines, Pavia University and Salinas datasets. As a result of experimental studies, 100% overall accuracy result is obtained in all datasets. The proposed 3DLeNet method is compared against various state-of-the-art CNN based methods. From the experimental results, it is seen that our 3DLeNet method performs more accurate result. It has also been found that the proposed 3DLeNet method shows a satisfactory result with less computational complexity.