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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/77713


    Title: 基於虹膜色彩空間的極端學習機的多類型頭痛分類;Multi-type Headache Classification Using ELM Based on Iris Color Space
    Authors: 羅菲倩;Atsiri, Rofiqoh Fithri
    Contributors: 資訊工程學系
    Keywords: 頭痛分類;虹膜色彩空間;ELM;headache classification;iris color space;ELM
    Date: 2018-08-09
    Issue Date: 2018-08-31 14:53:40 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 頭痛是人類最常見的疾病。頭痛患者常常出現幾種類型的頭痛,每種都有自己的症狀,在視覺上和身體上都很明顯,因此,頭痛的診斷是一個懸而未決的問題。對頭痛患者的初步觀察顯示症狀方面的虹膜變色。本研究通過將基本原色空間,色度和亮度以及人類視角色空間作為虹膜色彩空間的表示,以及試圖研究哪種色彩成分是解決該問題的最佳解決方案,提出了定量分類。
    ELM是單層前饋神經網絡的強大修改,用於對189個主題數據進行二元和多類分類,分發到162名頭痛患者和27名對照受試者。對應於使用ELM作為分類器的便利性,通過考慮ELM內隱藏節點的數量來比較分類性能。結果得出相對較好的結果,以區分對照受試者和頭痛患者,以及他們的頭痛類型。
    ;Headache is the most common illness for human. There are several types of headache commonly occurs among headache patients, each has their own symptoms which are visually and physically noticeable, hence, the headache diagnosis is an open problem. The preliminary observations on headache patients have shown iris-discoloration on the symptomatic side. This study proposes a quantitative classification by taking basic primary color space, chrominance and luminance, and human perspective color space as representation of iris color space, as well as trying to investigate which color components are the best solution to address the problem.
    ELM, a robust modification of single layer feedforward neural network, is implemented to do the binary and multiclass classification on 189 subject data, distributed into 162 headache patients and 27 control subjects. Corresponding to the conveniences of using ELM as classifier, the classification performance was compared by considering the number of hidden nodes inside ELM. The result obtained relatively good result to distinguish control subject and headache patient, along with their type of headache.
    Appears in Collections:[資訊工程研究所] 博碩士論文

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