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Works cited by this work

48 works

Work: Enhancing Medical Image Segmentation and Classification Using a Fuzzy-Driven Method

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  2. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

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  3. Concatenated CNN-Based Pneumonia Detection Using a Fuzzy-Enhanced Dataset

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  5. Untitled

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  7. Computer State Evaluation Using Adaptive Neuro-Fuzzy Inference Systems

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  8. Improving Fire Detection Accuracy through Enhanced Convolutional Neural Networks and Contour Techniques

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  9. Adaptive histogram equalization and its variations

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  10. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning

    Daniel Kermany, Michael H. Goldbaum, Wenjia Cai +36

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  11. Deep Learning in Medical Image Analysis

    Dinggang Shen, Guorong Wu, Heung‐Il Suk

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  12. CNN-Based Kidney Segmentation Using a Modified CLAHE Algorithm

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  13. Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-ray

    Tawsifur Rahman, Muhammad E. H. Chowdhury, Amith Khandakar +5

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  14. Deep-Pneumonia Framework Using Deep Learning Models Based on Chest X-Ray Images

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  15. Untitled

    Other3 citations
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  16. Image enhancement via adaptive unsharp masking

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  17. Adaptive Contrast Enhancement for Infrared Images Based on the Neighborhood Conditional Histogram

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  18. A Review of Deep-Learning-Based Medical Image Segmentation Methods

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  19. Integrating Color and Contour Analysis with Deep Learning for Robust Fire and Smoke Detection

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  20. Multi-Level Training and Testing of CNN Models in Diagnosing Multi-Center COVID-19 and Pneumonia X-ray Images

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  22. Kidney segmentation from computed tomography images using deep neural network

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  23. MSS U-Net: 3D segmentation of kidneys and tumors from CT images with a multi-scale supervised U-Net

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  24. A deep learning-based precision and automatic kidney segmentation system using efficient feature pyramid networks in computed tomography images

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  25. Deep Segmentation Networks for Segmenting Kidneys and Detecting Kidney Stones in Unenhanced Abdominal CT Images

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