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標題: 利用CNN進行白血球細胞之偵測與分類
Detection and classification of white blood cells using convolutional neural networks (CNN)
作者: 謝東陞
Tung-Sheng Hsieh
關鍵字: 捲積神經網路
Faster R-CNN
Convolutional neural networks (CNN)
white blood cell (WBC)
image recognition
Faster R-CNN
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摘要: 血液抹片檢查在臨床上的用途有許多種,包含了血球分類計數、骨髓檢查等。我們可以從血片中得知病人的血液狀況,以及有助於醫生診斷病情及了解治療效果。然而,這些工作是會耗費許多時間與人力的。所以本文所做的研究希望可以在白血球檢查這方面幫助到他們。 近年來,深度學習在機器學習中變得熱門,其中包含了捲積神經網路。捲積神經網路在影像辨識上的效果良好,本文以此方法來偵測與分類白血球細胞。我們使用了自己從台中慈濟醫院取得的資料庫來做實驗。為了能夠找出最有效率的網路,我們嘗試了各種不同的神經網路架構。除此之外,我們使用了Faster R-CNN來幫助我們偵測白血球細胞在血片影像上的位置。另外,我們也使用了Holdout驗證方法來確保我們實驗結果的可信度,而我們的辨識率能達到超過99%。
Blood film examination has many kinds of clinical application, including blood cell classification, blood cell count, and bone marrow examination. From the blood film, we can know the patient's blood condition. In addition, it can help doctors to diagnose the patient's condition and understand the effect of treatment. However, these jobs take a lot of time and labor. Therefore, we hope that the research in this thesis can help them for the white blood cell (WBC) examination. In recent years, deep learning has become popular in machine learning, including the convolutional neural networks (CNN). Convolutional neural networks have good performances on image recognition. In this thesis, we use this method to detect and classify white blood cells. The experiments were conducted by using the dataset that was taken from Taichung Tzu Chi Hospital. In order to find the most effective neural network, we have tried many different neural networks. In addition, we use the Faster R-CNN to help us to detect the white blood cells on the blood film image. Moreover, we also use the Holdout validation method to ensure the credibility of the experimental results, and the probability of the recognition can reach more than 99%.
文章公開時間: 2018-08-21
Appears in Collections:通訊工程研究所



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