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# System prepended metadata

title: Paper
tags: [陳]

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###### tags: ```陳```
# Paper
### (4) Exploiting ensemble learning for automatic cataract detection and grading
####  February 2016

- ALL
    - 眼底相機、1239圖像(767非)
    - 灰階、直方圖均衡化
    - 支援向量機、反向傳播神經網路
    - 疊加、多數投票組合出學習模型 

- On the other hand, common methods for cataract diagnosis require a slit lamp (e.g., the lens opacity classification system (LOCS III) [4] for clinical assessment or the Wisconsin cataract grading system (Wisconsin System) [5] for photographic grading), which is complicated and expensive for many patients.
    - [4] The lens opacities classification system III
    - [5] Cataract prevalence varies substantially with assessment systems: comparison of clinical and photographic grading in a population-based study

- Therefore, reducing costs and simplifying the process for early cataract diagnosis is a crucial means of improving eye care service in less developed areas and bringing light to cataract patients. 

- We know that ophthalmologists can diagnose cataracts by checking the clear degree of fundus images [3]
    - [3] Exploiting ensemble learning for automatic cataract detection and grading

- Thus, the task of fundus image classification is to build a classifier to simulate fundus image checking activities for automatic cataract detection and grading [1]
    - [1] Retinal imaging and image analysis

- Through a combination of three feature extraction methods (i.e., sketch, wavelet, and texture features) and two base learning models (i.e., SVM and BPNN), a total of six independent base classifiers are built. 
    - 採用三種特徵提取方法及兩種學習模型

- Unlike existing work on fundus image classification using a single learning model for cataract detection and grading, this paper proposes an ensemble learning approach that combines multiple heterogeneous learning models for more accurate prediction of cataract classification.
    - 提出一種結合多種學習模型的集合學習方法

-  Then, stacking and majority voting are adopted to combine the multiple base classifiers for improved fundus image classification.
    - 疊加、多數投票組合

### (5) Mobile Application Based Cataract Detection System 
#### 23-25 April 2019
- ALL 
    - OperCV
    - KNN分類
    - KNN better than SVM & NaiveBayes(不一定用得上)
- Geert et.al [11] in their paper entitled “A survey on deep learning in medical image analysis” have surveyed about deep learning algorithms. Deep learning algorithms are convolutional networks have significantly become a method of analyzing and studying medical images...
    - [11] A survey on deep learning in medical image analysis 

- The use and benefit of a deep learning algorithm are image classification, segmentation, object detection, registration, etc.
    - [11] A survey on deep learning in medical image analysis 

- A. Criminisi [14] in their paper entitled “ Machine Learning for Medical Image Analysis” has discussed the application and usage of machine learning in the analysis of images of the medical field 
    - [14] Machine Learning for Medical Image Analysis
    - 機器學習在醫療上的用途
- The proposed methodology is compared with SVM and Naïve Bayes, as the result shows the KNN based methodology outperforms
    - KNN > ...
    - CNN > KNN ? 
