以機器學習搭配表面增益拉曼光譜診斷癌症

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姓名

金亞煇(Kim Nhat Huy) 查詢紙本館藏

畢業系所

光電科學與工程學系

論文名稱

以機器學習搭配表面增益拉曼光譜診斷癌症
(Applying Machine Learning in Surface-Enhanced Raman Spectroscopy for Cancer Diagnosis)

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至系統瀏覽論文 (2026-1-1以後開放)

摘要(中)

本研究分析四種癌症（乳癌、子宮內膜癌、肺癌、胰腺癌）的人體血漿，利用表面增強拉曼光譜(Surface-Enhanced Raman Spectroscopy, SERS)結合機器學習，目的是要開發高速、精準的血液感測晶片。我們採用的SERS結構含有多層InGaN量子井，根據69例血漿檢體(乳癌10例、子宮內膜癌10例、肺癌29例、胰臟癌10例、健康對照組10例)的分析結果，本技術能有效檢測出這四種癌，準確率可達96%。

摘要(英)

This research focuses on analyzing human plasma associated with four cancers (breast, endometria, lung, pancreas), using the surface-enhanced Raman spectroscopy (SERS) integrated with machine learning for prediction and classification. The SERS structure comprises multiple InGaN quantum wells. According to the test results of 69 clinical cases (10 cases of breast cancer, 10 cases of endometrial cancer, 29 cases of lung cancer, 10 cases of pancreatic cancer, and 10 cases of health control), our technique can quickly identify the types of cancer with the average prediction accuracy of 96%.

關鍵字(中)

★ 臨床診斷
★ 癌症
★ 氮化物半導體
★ 量子阱
★ 表面增強拉曼光譜
★ 機器學習

關鍵字(英)

★ clinical diagnosis
★ cancer
★ nitride semiconductor
★ quantum well
★ surface-enhanced Raman spectroscopy
★ machine learning

論文目次

中文摘要 i
Abstract ii
Acknowledgements iii
List of Figures v
List of Tables vi
List of Abbreviations vii
Chapter 1. INTRODUCTION 1
1.1. Overview and thesis motivation 1
1.2. Theory of surface-enhanced Raman spectroscopy 2
1.3. Machine learning integrated with SERS 5
1.4. Thesis objective 9
Chapter 2. EXPERIMENT 10
2.1. Clinical samples preparation 10
2.2. SERS chips properties 11
2.3. SERS measurement protocol 12
2.4. Data treatment and statistical analysis 12
Chapter 3. RESULTS AND DISCUSSION 16
3.1. SERS signatures 16
3.2. Model evaluation 21
Chapter 4. CONCLUSION AND FUTURE WORKS 32
4.1. Conclusion 32
4.2. Future works 32
References 33

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指導教授

賴昆佑簡汎清(Kun-Yu Lai Fan-Ching Chien)

審核日期

2024-12-30

推文