喉部症狀以內視鏡觀測時會依不同醫師間有不同標準，對檢查結果非常仰賴檢查者本身經驗之主觀判定，使得不同研究間的數據和結果幾乎不相容。本研究目的是開發據客觀標準之可偵測喉部病變電腦輔助診斷系統，病變包括聲帶息肉、聲帶囊腫、聲帶白斑、聲帶腫瘤以及逆流性咽喉炎等，並針對逆流性咽喉炎作嚴重程度分級。
本研究針對喉部病變對咽喉造成的變化進行量化，利用在內視鏡影片中咽喉特徵結構進行自動部位分割。採用包含杓狀軟骨、聲門、左聲帶和右聲帶等區域的色相、紋理及幾何分析，藉由影像處理技術分析喉部影像特徵便能診斷喉部病變。本研究亦測試色相和紋理共36種特徵，使用費雪線性判別篩選對逆流性咽喉炎具分類性能的特徵。
本研究開發喉病變檢測系統共459份樣本，包含逆流性咽喉炎、聲帶息肉、囊腫、白斑和腫瘤等病變。聲帶病變分類準確度為97.45%。而逆流性咽喉炎檢測應用支持向量機(Support Vector Machine, SVM)，藉由準確度、敏感度和偽陽性率三種方式進行評估結果。評估結果分別為準確度97.16%、敏感度98.11%和偽陽性率3.77%。本研究使用人工類神經網路(Artificial Neural Network, ANN)作為逆流性咽喉炎之分類嚴重程度的方式，其準確度96.48%。

The symptoms of the larynx are observed by endoscopes according to different standards. The results of the examination depend on the subjective judgment of the examiner's own experience, making the data and results of different studies almost incompatible. The purpose of this study was to develop a computer-aided diagnosis system for detectable laryngeal lesions based on objective criteria, including vocal cord polyps, vocal cord cysts, vocal cord leukoplakia, vocal cord tumors, and reflux pharyngitis, and to grade the severity of reflux pharyngitis. .
In this study, the changes in the throat caused by laryngeal lesions were quantified, and the automatic segmentation of the throat features in the endoscopic film was performed. Laryngeal lesions can be diagnosed by image processing techniques using hue, texture, and geometric analysis of areas including sacral cartilage, glottis, left vocal cords, and right vocal cords. This study also tested 36 features of hue and texture, using Fisher's linear discriminant screening to characterize the classification performance of reflux pharyngitis.
This study developed a total of 459 samples of laryngeal lesion detection system, including reflux pharyngitis, vocal cord polyps, cysts, leukoplakia and tumors. The classification accuracy of vocal cord lesions was 97.45%. The support vector machine (SVM) was applied to the detection of reflux pharyngitis. The results were evaluated by accuracy, sensitivity and false positive rate. The evaluation results were 97.16% accuracy, 98.11% sensitivity and 3.77% false positive rate. In this study, artificial neural network (ANN) was used as the classification severity of reflux pharyngitis, with an accuracy of 96.48%.

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