現代醫學中，細胞腫瘤為一種複雜的疾病，而腫瘤細胞的切片影像則能為醫生提供腫瘤內各類型的細胞組成以及分布概況，並可透過此影象對病患做初步的病症判讀。本研究結合機器學習及人工智慧的方式，設計一套腦腫瘤類型的分類系統，透過自動化的處理對腦腫瘤的切片影像進行自動分類。然而機器學習的設計方法也有其受限之處，在現實環境中，臨床病患的個人資料不易取得，而不同來源的資料也有品質不一的問題，在這種情況下所訓練出的模型可能會造成預測結果偏頗或是不佳的狀況，因此本研究中使用了不同的影像處理及訓練方法使訓練出的模型能在資料受限的狀況下有穩定的判別能力，此外，我們設計了三種實驗探討系統中各項方法對於預測結果的影響。

Cellular tumors are a complex disease, and the histological slides of tumor cells allow doctors to observe the composition and distribution of various types of cells in the tumor, and make initial diagnosis of the disease through this image. Advancements in digital microscopy enables digitization of histology slides and histopathology diagnosis combined with image analysis is an emerging application of machine-learning techniques. In this study, we designed a classification system for brain tumor slides, aiming to automatically classify brain tumor types. However, the design method of machine learning also has its limitations. The quality of data from different sources is also different. The model obtained by data-driven methods may be influenced by data quality and then produce inaccurate results. In this study, we performed three experiments to explore the influence of machine-learning methods and developed a fully automatic system for histopathology diagnosis.

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