這份論文提出一個電腦輔助診斷的系統，透過特徵選擇演算法 sequential forward floating selection (SFFS)暨支援向量機，來輔助醫師，直接從胃部內視鏡影像中讀出組織學上的結果，避免因組織切片所造成的傷害與傳統上組織培養所耗費的時間。
　　在取得的內視鏡影像中，我們首先利用數位小波轉換、顏色及材質等方法來取得所需的影像特徵，由於取得的影像特徵數目眾多且無法有效表達內視鏡影像的內容，我們利用特徵選擇演算法SFFS暨支援向量機來找出在分類上最有意義的影像特徵。接下來再以這些選取出來的特徵作為支援向量機輸入而得到組織學上的結果。
　　透過這些方法，我們實做出一個新的診斷系統，可以輔助醫師立即從內視鏡影像中判別出組織學上的結果，而不需要耗時的切片培養。

　　This study presented a computer-aided diagnosis system using sequential forward floating selection (SFFS) with support vector machine (SVM) to assist physicians to obtain gastric histology from endoscopic images without invasive biopsy during endoscopy. At first, several images features of endoscopic images are extracted via discrete wavelet transform, color and texture criterion. Then, SFFS is applied to select a subset of features, which performs the best classification result under SVM. Based on this methodology, a new diagnosis system is implemented to provide physicians the instant gastric histology results during the endoscopy without invasive biopsy.

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