| 摘要: | 隨著科技日益發展，人工智慧扮演了很重要的角色，機器的優勢在於能重複工作並且也不會疲乏，近年來許多人開始探討如何讓機器有著像人類的智慧使得這領域在這幾年快速發展。機器學習跟感知與估測扮演了很重要的角色，其中機器學習又可分為四種:監督式學習、 非監督式的學習、半監督式的學習、增強式學習。其中神經網路在機器學習上扮演了很重要的角色。感知跟估測可以藉由已知的資訊去推得更多未來的資訊。
影像辨識在人工智慧扮演了很重要的角色，例如:動物辨識、手寫辨識、車牌辨識。使用深度學習最主要的目的在於能夠提取特徵並能降低成本，但要做出好的分類並不是這麼容易，有很多因素都會相互影響著。例如:電腦設備、參數設定、優化器選取、模型架構。
本實驗肺炎圖片來自
https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia
且使用卷積神經網路的方式去建立肺炎辨識模型， 並選定幾種方式可能會影響肺炎辨識的原因作為討論、分析的對象。由實驗結果發現，dropout比例大小、優化方法、遷移學習、凍結參數、卷積層層數都會影響模型的表現能力。
;With the development of science and technology, artificial intelligence has played a very important role. The advantage of machines is that they can repeat work without fatigue. In recent years, many people have begun to discuss how to make machines have human-like intelligence to make this field fast developed in these years. Machine learning plays a very important role in perception and estimation. Among them, machine learning can be divided into four types: supervised learning, unsupervised learning, semi-supervised learning, and enhanced learning. Among them, neural networks play a very important role in machine learning. Perception and estimation can use the known information to deduce more future information.
Image recognition plays an important role in artificial intelligence, such as animal recognition, handwriting recognition, and license plate recognition. The main purpose of using deep learning is to be able to extract features and reduce costs, but it is not so easy to make a good classification, many factors will affect each other. For example: computer equipment, parameter setting, optimizer selection, model architecture.
The pictures of pneumonia in this experiment come from kaggle, and use the method of convolutional neural network to establish a pneumonia identification model, selecting several ways that may affect the identification of pneumonia as the object of discussion and analysis. From the experimental results, it is found that the dropout ratio, optimization method, transfer learning, freezing parameters, and the number of convolutional layers all affect the performance of the model. Keywords: machine learning, volume-based layered network, deep learning, reinforcement learning, optimizer, filter, transfer learning. |
