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Huseyin Uzen
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2020 – today
- 2024
- [j11]Hüseyin Üzen
:
ConvMixer-based encoder and classification-based decoder architecture for breast lesion segmentation in ultrasound images. Biomed. Signal Process. Control. 89: 105707 (2024) - [j10]Hüseyin Üzen
Swin transformer-based fork architecture for automated breast tumor classification. Expert Syst. Appl. 256: 125009 (2024) - [j9]Ayse Ayyüce Demirbas, Hüseyin Üzen
Spatial-attention ConvMixer architecture for classification and detection of gastrointestinal diseases using the Kvasir dataset. Health Inf. Sci. Syst. 12(1): 32 (2024) - [j8]Hüseyin Üzen
A hybrid approach based on multipath Swin transformer and ConvMixer for white blood cells classification. Health Inf. Sci. Syst. 12(1): 33 (2024) - [j7]Hüseyin Firat, Hüseyin Üzen, Davut Hanbay, Abdulkadir Sengür:
ConvNext Mixer-Based Encoder Decoder Method for Nuclei Segmentation in Histopathology Images. Int. J. Imaging Syst. Technol. 34(5) (2024) - [j6]Onur Polat
Multi-Stage Learning Framework Using Convolutional Neural Network and Decision Tree-Based Classification for Detection of DDoS Pandemic Attacks in SDN-Based SCADA Systems. Sensors 24(3): 1040 (2024) - [j5]Hüseyin Üzen, Muammer Turkoglu, Dursun Öztürk, Davut Hanbay:
A novel hybrid attention gate based on vision transformer for the detection of surface defects. Signal Image Video Process. 18(10): 6835-6851 (2024) - [c2]Berna Gürler Ari, Hüseyin Üzen, Abdulkadir Sengür:
Accurate Segmentation of Brain Tumors in Magnetic Resonance Images with Pyramid Stage Decomposition Network Approach. SIU 2024: 1-4 - 2023
- [j4]Huseyin Uzen
Multi-dimensional feature extraction-based deep encoder-decoder network for automatic surface defect detection. Neural Comput. Appl. 35(4): 3263-3282 (2023) - [j3]Hüseyin Üzen
Depth-wise Squeeze and Excitation Block-based Efficient-Unet model for surface defect detection. Vis. Comput. 39(5): 1745-1764 (2023) - 2022
- [b1]Hüseyin Üzen:
İmalat sistemlerinde derin öğrenme tabanlı doku hata tespiti (Deep learning based texture defect detection in manufacturing systems). İnönü University, Turkey, 2022 - [j2]Hüseyin Üzen
Swin-MFINet: Swin transformer based multi-feature integration network for detection of pixel-level surface defects. Expert Syst. Appl. 209: 118269 (2022) - 2021
- [j1]Huseyin Uzen
Texture defect classification with multiple pooling and filter ensemble based on deep neural network. Expert Syst. Appl. 175: 114838 (2021) - [c1]Hüseyin Üzen
Surface Defect Detection Using Deep U-Net Network Architectures. SIU 2021: 1-4
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last updated on 2024-12-08 01:32 CET by the dblp team
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