Automatic Feature Learning for Glaucoma Detection Based on Deep Learning論文理解
本文提出了基於DeepLearnINg的自動特徵學習爲青光眼檢測，不同於傳統的卷積神經網絡，本文采用的網絡嵌入了微型神經網絡（多層感知器）有更復雜的結構去抽象感知區域的數據；此外提出了一種上下文深度學習結構來獲得眼底圖像的分層表示以區分青光眼和非青光眼模式，這裏網絡採用其他CNN網絡的輸出作爲此網絡的上下文特徵來提升青光眼檢測的性能。本文采用的方法在ARIGA和SCES數據上進行測試
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相關文章
- 1. Summary of defect detection algorithms based on deep learning
- 2. 對 Detection of Malicious Code Variants Based on Deep Learning 的簡單理解
- 3. A Deep Learning-Based System for Vulnerability Detection
- 4. 論文閱讀- Segmentation-Based Deep-Learning Approach for Surface-Defect Detection
- 5. 論文閱讀筆記《Segmentation-based deep-learning approach for surface-defect detection》
- 6. 【醫學+深度論文：F18】2019 Robust optic disc and cup segmentation with deep learning for glaucoma detection
- 7. 論文理解 Deep Residual Learning for Image Recognition
- 8. 【論文翻譯】Deep Residual Learning for Image Recognition
- 9. Gan-based zero-shot learning 論文整理
- 10. Machine Learning-based Web Exception Detection