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<table class="tg">
<thead>
  <tr>
    <th class="tg-dvpl">ARTICLE NAME             </th>
    <th class="tg-0pky">AUTHOR</th>
    <th class="tg-0pky">METHODOLOGY</th>
    <th class="tg-0pky">I.PROS<br>II.CONS</th>
  </tr>
</thead>
<tbody>
  <tr>
    <td class="tg-0pky">"Identification of subjects<br>with polycystic ovary <br>syndrome using electronic<br>health records" <br>Reproductive biology and<br>endocrinology(2015)</td>
    <td class="tg-0pky">Victor M<br>castro,<br>Yuanyuan <br>Shen,Sheng<br>Yu et al[11]</td>
    <td class="tg-0pky">NLP</td>
    <td class="tg-0pky">I.NLP takes free tent and <br>codifies the data into <br>computationally functional<br>categories.<br><br>II.The positive values should<br>be improvised.</td>
  </tr>
  <tr>
    <td class="tg-0pky">"An analysis of PCOS <br>disease prediction<br>model using machine learning <br>classification<br>algorithms" Recent patents on <br>Engineering(2021)</td>
    <td class="tg-0pky">Shuvani Aggarwal<br>and Kavita Pandey</td>
    <td class="tg-0pky">Machine learning<br>algorithms</td>
    <td class="tg-0pky">I.12 major attributes for<br>detecting PCOS has been<br>identified.<br><br>II.There is a high level of <br>error susceptibility.</td>
  </tr>
  <tr>
    <td class="tg-0pky">"Genetic clustering for<br>PCOS detection in women<br>reproductive age" IJEAT-ISSN:2249<br>-8958,Volume-9 Issue-3,<br>February(2020)</td>
    <td class="tg-0pky">Anuradhan D.Thakure<br>and Priyanka R.Lele</td>
    <td class="tg-0pky">Genetic operators<br>and Machine learning <br>algorithms</td>
    <td class="tg-0pky">I.Accuracy 89%, reduced<br>error rate.</td>
  </tr>
  <tr>
    <td class="tg-0pky">"Study and detection of PCOS<br>related disease using CNN" <br>IOP Conference series: Materials<br>science Engineering, Volume 1070<br>-(ICRIET 2020)<br></td>
    <td class="tg-0pky">M Sumathi,P Chithra,<br>R Sakthi prabha and<br>Srilatha K</td>
    <td class="tg-0pky">CNN based algorithms,<br>extraction features<br>process by watershed <br>algorithm,coding in <br>python</td>
    <td class="tg-0pky">I.It is easy to understand<br>and fast to implement,it <br>has high accuracy<br><br>II.It requires a large dataset<br>process and train neural network</td>
  </tr>
  <tr>
    <td class="tg-0pky">"Automatic segmentation of<br>ovarian follicles using <br>K-means clustering" ResarchGate<br>publication(2015)</td>
    <td class="tg-0pky">Ramya MM and <br>Kiruthika V</td>
    <td class="tg-0pky">K-means clustering</td>
    <td class="tg-0pky">I.This method serves as a basis<br>for fully automatic detection<br>of follicles.</td>
  </tr>
  <tr>
    <td class="tg-0pky">"An implementation of Elman<br>neural network for polycystic <br>ovary classification based on <br>ultrasound images" Journal of <br>Physics: Conference Series, Vol.971<br>conference 1(2018)</td>
    <td class="tg-0pky">Thufailah,Adiwijaya,<br>Wisesty and Jondri</td>
    <td class="tg-0pky">Elman neural networks</td>
    <td class="tg-0pky">I.Accuracy of 78.1% with <br>32 features.<br><br>II.Using more data for <br>training will affect the <br>time process</td>
  </tr>
  <tr>
    <td class="tg-0pky">"Image segmentation for<br>detecting PCOD using deep <br>neural networks" IJCSE-Vol-7,Issue<br>-3,March(2019)<br></td>
    <td class="tg-0pky">Palvi Soni and <br>Sheveta Vashisht</td>
    <td class="tg-0pky">MATLAB,KNN algorithm</td>
    <td class="tg-0pky">I.Minimum execution time,high<br>accuracy.<br><br>II.Over segmentation when image <br>has intensity variation causes <br>more time.</td>
  </tr>
  <tr>
    <td class="tg-0pky">"An automated diagnostic system<br>of polycystic ovary syndrome <br>based on object growing" Artif Intell <br>Med.(2011)Mar</td>
    <td class="tg-0pky">Deng Y,Wang Y<br>and Shen Y</td>
    <td class="tg-0pky">Object growing<br>algorithm</td>
    <td class="tg-0pky">I.Proposed system is highly <br>effective in follicle <br>identification for PCOS(89.4%<br>recognition rate)</td>
  </tr>
  <tr>
    <td class="tg-0pky">"Ovarian follicle detection for<br>polycystic ovary syndrome <br>using fuzzy C-means clustering" IJCTT-<br>Volume 4-Issue 7-July(2013)</td>
    <td class="tg-0pky">Ashika Raj</td>
    <td class="tg-0pky">C-means clustering</td>
    <td class="tg-0pky">I.It gives best result for <br>overlapped dataset and <br>comparatively better <br>than k-means.<br><br>II.Apriori specification of the<br>number of clusters. </td>
  </tr>
  <tr>
    <td class="tg-0pky">"Value of ultrasonography in the <br>diagnosis of polycystic ovary<br>syndrome-literature review" Journal of <br>Ultrasonography(2015)</td>
    <td class="tg-0pky">Michal Bachanek,<br>Nebil Abadalla,<br>Krysztof Cendrowski,<br>Wlodzimierz Sawiciki</td>
    <td class="tg-0pky">Ultrasonography</td>
    <td class="tg-0pky">I.It provides clear image of <br>soft tissues, less expensive.<br><br>II.Hard tissue cannot be imaged.<br>In women less than 20years,<br>ultrasound are not recommended.<br></td>
  </tr>
</tbody>
</table>
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