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| Title: | X光醫學影像的辨識與統計分析
A Statistical Approach of X-ray Medical Image Recognition |
| Authors: | 陳品華
Chen, Pin-Hua |
| Contributors: | 余清祥
陳品華
Chen, Pin-Hua |
| Keywords: | 影像辨識
醫學影像
統計分析
維度縮減
機器學習
Image Recognition
Medical Image
Statistical Analysis
Dimensionality Reduction
Machine Learning |
| Date: | 2025 |
| Issue Date: | 2025-09-01 14:49:06 (UTC+8) |
| Abstract: | 影像辨識是人工智慧的重要應用之一，辨識手寫郵遞區號是知名範例，透過光學掃描再以電腦判讀，可大幅節省郵務的工作時間。近年電腦科技進步使得影像辨識的準確度及效率顯著提升，因此應用層面更為廣泛，像是機場掃描人臉通關、車牌辨識都是眾所皆知的應用。本文以醫學影像的病例分類為研究目標，引進統計分析的概念分辨肺炎感染者，並標示出可能異常位置，可供醫師判讀病患狀況。由於肺炎患者之肺紋會因密度增加造成毛玻璃現象，紋理變化可藉由方向梯度直方圖（Histogram of Oriented Gradients, HOG）等方法萃取其特性，本文先提取 HOG 在 12 個方向上的梯度變化累計，再透過統計、機器學習模型進行分類，可獲得較佳的準確率。此外，本文也考量分類結果與方向數量、切割區塊的關係，因為解析度扮演重要角色。結果顯示本文使用36 個解釋變數即可達到不錯的準確率，並兼顧維度縮減及運算效率，而且挑選出之解釋變數可作為醫師臨床診斷的參考。
Image recognition is one of the key applications of artificial intelligence. A well-known example is the recognition of handwritten postal codes, where optical scanning combined with computer interpretation can significantly reduce the workload of postal services. In recent years, advancements in computer technology have greatly improved the accuracy and efficiency of image recognition, leading to wider applications such as airport facial recognition and license plate recognition. This study focuses on classifying medical images of clinical cases, introducing statistical analysis to distinguish patients with pneumonia and highlight potential abnormal regions for physicians to interpret patient conditions. Due to increased lung texture density in pneumonia patients causing ground-glass opacity, texture variations can be captured using methods such as Histogram of Oriented Gradients (HOG). In this study, HOG features in 12 directions are extracted and accumulated, followed by classification using statistical and machine learning models to achieve better accuracy. Moreover, this study examines the relationship between classification performance and the number of gradient directions as well as image partitioning, since resolution plays a crucial role. The results show that using only 36 explanatory variables can achieve excellent accuracy while balancing dimensionality reduction and computational efficiency. The selected explanatory variables may also serve as references for clinical diagnosis by physicians. |
| Reference: | 一、中文文獻
[1] 陳慧霜（2023），「影像分析與深偽影片的偵測」，國立政治大學統計學系學位論文。
[2] 黃政嘉（2024），「Deepfake 與 GAN真偽圖像統計分析」，國立政治大學統計學系學位論文。
[3] 張倉銓（2022），「深度學習訓練效率的模糊驗證模式—以田口優化的卷積神經網路在辨識肺癌醫學影像為例」。國立臺中科技大學資訊工程系碩士論文。
[4] 郭立杰、許嘉汀（2022），「基於人工智慧的皮膚病理影像辨識」。收錄於《南臺灣醫學雜誌》，18（1），17‑27。
二、英文文獻
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[14] Vats, S., Sharma, V., Singh, K., Katti, A., Mohd Ariffin, M. M., Nazir Ahmad, M., Ahmadian, A., & Salahshour, S. (2024).Incremental learning‑based cascaded model for detection and localization of tuberculosis from chest X‑ray images. Expert Systems with Applications, 238, Article 122129.
[15] Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017). ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3462-3471.
[16] Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017). ChestX‑ray8: Hospital‑scale chest X‑ray database and benchmarks on
weakly‑supervised classification and localization of common thorax diseases. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3462–3471). |
| Description: | 碩士
國立政治大學
統計學系
112354010 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0112354010 |
| Data Type: | thesis |
| Appears in Collections: | [統計學系] 學位論文
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