A Comprehensive Study of Imaging and Machine Learning Techniques for Diagnosis the Disease

P Illavarason; J Jeyachidra; T Logest

Proceedings of the First International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICASISET 2020, 16-17 May 2020, Chennai, India

Research Article

A Comprehensive Study of Imaging and Machine Learning Techniques for Diagnosis the Disease

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@INPROCEEDINGS{10.4108/eai.16-5-2020.2304152,
    author={P  Illavarason and J  Jeyachidra and T  Logest},
    title={A Comprehensive Study of Imaging and Machine Learning Techniques for Diagnosis the Disease},
    proceedings={Proceedings of the First  International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICASISET 2020, 16-17 May 2020, Chennai, India},
    publisher={EAI},
    proceedings_a={ICASISET},
    year={2021},
    month={1},
    keywords={digital image processing methododlogy classification methodology machine learning analysis analysing the improvement cp children},
    doi={10.4108/eai.16-5-2020.2304152}
}

P Illavarason
J Jeyachidra
T Logest
Year: 2021
A Comprehensive Study of Imaging and Machine Learning Techniques for Diagnosis the Disease
ICASISET
EAI
DOI: 10.4108/eai.16-5-2020.2304152

P Illavarason¹^,*, J Jeyachidra¹, T Logest¹

1: Periyar Maniammai Institute of Science & Technology, Periyar Nagar, Vallam-613 403, Thanjavur, Tamilnadu, India

*Contact email: illavarason@pmu.edu

Abstract

Cerebral Palsy (CP) is described as a neurological disorder due to the abnormalities that develop during brain development in kids. This paper represented the past work on Iris detection and machine learning classification techniques. The experimental result shows the best accuracy obtained for detection of iris and eye corner by our algorithm for the real world conditions with state of art methodology.

Keywords: digital image processing methododlogy classification methodology machine learning analysis analysing the improvement cp children

Published: 2021-01-27
Publisher: EAI

: http://dx.doi.org/10.4108/eai.16-5-2020.2304152