---
# System prepended metadata

title: 文獻探討paper整理
tags: [總審]

---

# 文獻探討paper整理

###### tags: `總審`

## paper 名稱～（麻煩幫我把作者、出版日寫出來）
### 重點內容

==1-1&1-3刪除==

## (1)Cataract Detection using Digital Image Processing
### Oct 18-20, 2019

I. Jindal, P. Gupta and A. Goyal, "Cataract Detection using Digital Image Processing," 2019 Global Conference for Advancement in Technology (GCAT), 2019, pp. 1-4, doi: 10.1109/GCAT47503.2019.8978316.

**Two Method**
- First (feature extraction based automated detection algorithm)
    - Preprocessing (used for the extraction of important features)
        - the image is **cropped** to extract the pupil and is **resized** to enlarge it
        - **converted to grayscale** and enhanced by applying the Gaussian filter
    - Feature Extraction
        - evaluating the texture feature parameter using image **intensity**.The texture feature used for the analysis is **mean(m)**.
        - **unhealthy eyes will have a higher mean** value of intensity.
    - Decision Making
        - based on certain **threshold** values
        - the mean intensity of a **healthy eye image falls below 50** whereas the ones severely clouded and affected by **cataract have a mean value over 100**.

- Second (Area Calculation)
    - 前處理的部分都跟第一個方法一樣
    - Degree = (cataract area / (pupil area + cataract area)) * 100
    - 這方法有講到計算發病的範圍（白內障覆蓋程度），可以延伸我們之前說想做**判讀白內障嚴重程度**。

## (1-2)Analysis and Study of Cataract Detection Techniques
### 2016


D. Patil, A. Nair, N. Bhat, R. Chavan and D. Jadhav, "Analysis and study of cataract detection techniques," 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), 2016, pp. 516-519, doi: 10.1109/ICGTSPICC.2016.7955355.

- Cataract is classified into three types based on **the area of protein deposition** named as **nuclear cataract**, **cortical cataract**, **posterior sub- capsular cataract**.
    - Nuclear cataract as the name suggests develops on the **nucleus of the lens**. It is normally caused in **aged people**
    - Cortical cataract develops in the **cortex of the lens which surrounds the lens**
    - Posterior sub-capsular cataract develops at the **back of the lens**. People suffering from diabetes and those taking **steroids**(類固醇) are at risk of having such cataracts.
- Preprocessing, Feature Extraction, Classifier construction.
- ophthalmoscope(眼底鏡) is used to create the red reflex effect. **A ray of light reflects from the back of the eyeball** and through the pupil to create an optical pathway which helps to detect cataract based on the factors that block this pathway.
- Based on the analysis and study of different techniques of cataract detection, it was found that **using fundus images and having Adaboost algorithm as a classifier provided the most accurate results** compared to other techniques that were studied upon.
- Slit lamp imaging method is mostly used which is expensive and less accurate. It can only detect and grade a specific class of cataract. The fundus image based cataract system is also studied. Its efficient but not portable and consumes time. Hence there is a **need of a system which has an efficient algorithm and also is portable that is used in smart-phones by remote people with minimum amount of detection time**. ==(我覺得這裡可以延伸因為透過專業器材做預測太困難耗時了，所以我們轉向尋找有無可以從外面直接做判斷的，然後我們就找到～bla bla bla～的paper)==

--- 
    白內障是造成失明的主要起因之一，特別在年長者間較嚴重。根據2010年WHO的統計，全球約有3900萬人口失明，其中51%的失明人口即是因為罹患白內障。若能提前察覺，即能避免完全性失明。
    此篇論文中，透過「訓練人工智慧判斷是否罹患白內障」為起點，收集國內外的相關文獻作為參考。首先，本論文對於白內障檢測(Cataract Detection)做文獻收集，內容包括現今檢測罹患白內障的方法、使用機器學習和深度學習的檢測方法與方向，還有未來發展和面臨到的困境。其中，～有高達～的辨識率。本論文透過GitHub上Krishnabojha提供的照片資料集，並使用卷積神經網路來訓練能夠辨識是否罹患白內障的人工智慧。
