傳統的電腦輔助診斷(CAD) 系統包含許多影像處理和圖形識別步驟去想出一個定量的腫瘤區別結果。在這樣特別的影像分析流程，每個階段都高度依賴著前一個步驟產生的結果。因此，在調整傳統CAD 系統的分類效果是非常複雜且困難的。另一方面，深度學習(deep learning) 擁有從本質上的優點，自動地開發特徵和用無縫方式調整效能。在本篇研究中，我們嘗試使用深度學習技術來簡化傳統的CAD 影像分析流程。具體來說，我們在CT 影像中的結節(nodule) 分類情境中導入deep believe network(DBN) 和convolutional neural network(CNN) 方法。實作了兩種基本的特徵抓取方法來做比較。實驗結果顯示深度學習方法達到更好的分類
結果。因此證實了深度學習在CAD 應用方面的效用。

Conventional computer-aided diagnosis (CAD) scheme comprises several image processing and pattern recognition steps to come up with a quantitative tumor differentiation result. In such an ad-hoc image analysis pipeline, every latter step highly depends on the performance of previous step. Accordingly, it turns out that the tuning of the classification performance in conventional CAD scheme is very complicated and arduous. The deep learning techniques, on the other hand, have intrinsically advantage of automatic exploit feature and performance tuning in a seamless fashion. In this study, we attempt to simplify the image analysis pipeline of conventional CAD with the deep learning techniques.
Specifically, we introduce the models of deep believe network (DBN) and convolutional neural network (CNN) into the context of nodule classification in CT images. Two baseline methods with feature computing steps are implemented for comparison. The experimental results suggest the deep learning methods achieve better discriminative results. Accordingly, the effectiveness of deep learning techniques in CAD application domain is substantiated.

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