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# System prepended metadata

title: paper整理
tags: [洪]

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# paper整理
###### tags: `洪`




## Paper
### (6)Cataract Detection Using Single Layer Perceptron Based on Smartphone

### 29-30 Oct. 2019

* This chapter discusses the method used to create a cataract detection system using a single perceptron on a smartphone. 
  * 步驟:
    1. Input Image
    2. Preprocessing(Cropping Image、Grayscale、 Median Filter)
    3. Segmentation(Canny Edge Detection、Hough Circle Transform)
    4. Feature Extraction(Mean Intensity、Uniformity)
    5. Classification(kNN)
* 相關工作
   * The preprocessing process starts with the process of cropping and resizing the image, do a Grayscale, and the Median filter.
   * The segmentation process uses several methods, namely Canny Edge detection, Hough circular transform, and cropping on the pupil area.
   * For the feature extraction stage using the gray histogram and get features in the form of mean intensity and uniformity. From the extraction results, the training data uses the single layer perceptron method, then used to determine the classification results in several eye forms, such as normal eyes, immature cataract eyes, and mature cataract eyes.
   * This system can be used for cataract examination by everyone. If it is detected cataracts are expected to immediately conduct further examinations to the ophthalmologist to get treatment before getting severe. The difference between this system and previous research is that this system uses more training data and data is taken directly from cataract patients with smartphone.
   * Some parameters that affect the results of accuracy using KNN as a classification method, namely the relevant features to be used and the k value of K-NN.
     * KNN會影響某些參數

### (7)Machine Learning on Cataracts Classification Using SqueezeNet
### 21-24 Oct. 2018
* To classify different areas of cataracts in lens, we use supervised training of convolutional neural network to train 420 images of cataracts on the lens taken from slit-lamps. 
  * 使用CNN訓練
* The experiment can make the future of classifying cataracts more easily and ophthalmologists can apply operations to different categories of cataracts within a shorter time to cure patients with cataracts. For those people in the countryside, even not so experienced doctors can take the photo of lens and use the program to classify cataracts correctly. 

  * 該實驗使白內障分類更加輕鬆、眼科醫生可以在更短的時間內對不同類別的白內障進行手術，以治愈白內障患者
* First we got our dataset with labels for training and did some image preprocessing such as cropping and image generating methods.
  * 對數據集圖像做預處理 EX.Cropping、mage generating method
* We then trained new neural network layer on top of SqueezeNet, which is a pre-trained model that already achieved high accuracy on classifying images in ImageNet.
  * 在SqueezeNet上訓練新的神經網路
  * SqueezeNet是一種預先訓練的模型，已經在ImageNet中對圖像進行分類時實現了高準確度
* SqueezeNet is one type of convolutional neural network (CNN), which achieves the same accuracy on classifying images from ImageNet as AlexNet but needs 50 times fewer parameters than AlexNet. It can be compressed to 510 times smaller than AlexNet. This provided a quicker and more accurate result of the training model.
  * SqueezeNet是卷積神經網絡（CNN）的一種，它在對ImageNet的圖像進行分類時達到與AlexNet相同的精度，但所需參數比AlexNet少50倍。它可以壓縮到比AlexNet小510倍。這為訓練模型提供了更快，更準確的結果。
  * SqueezeNet與其他CNN模型的不同之處在於，有一個構建模塊稱為火災模塊。
 