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Reliability Assessment of Machine Learning in Tumour Detection

by Vedant Gadhavi, Arth Anant, Darshit Shah
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 38
Year of Publication: 2022
Authors: Vedant Gadhavi, Arth Anant, Darshit Shah
10.5120/ijca2022922468

Vedant Gadhavi, Arth Anant, Darshit Shah . Reliability Assessment of Machine Learning in Tumour Detection. International Journal of Computer Applications. 184, 38 ( Dec 2022), 24-30. DOI=10.5120/ijca2022922468

@article{ 10.5120/ijca2022922468,
author = { Vedant Gadhavi, Arth Anant, Darshit Shah },
title = { Reliability Assessment of Machine Learning in Tumour Detection },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2022 },
volume = { 184 },
number = { 38 },
month = { Dec },
year = { 2022 },
issn = { 0975-8887 },
pages = { 24-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number38/32564-2022922468/ },
doi = { 10.5120/ijca2022922468 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:23:29.067588+05:30
%A Vedant Gadhavi
%A Arth Anant
%A Darshit Shah
%T Reliability Assessment of Machine Learning in Tumour Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 38
%P 24-30
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

It is vital that tumours are diagnosed and predicted early in cancer research to help the patient clinically. In today’s world, innovative approaches are being developed to minimise or avoid lethal human diseases. Machine Learning is becoming increasingly popular for classifying cancer patients according to their risk of recurrence. Machine learning expands its applications beyond the technical domain, and its pertinence in the medical area is also proliferating. It can also be used in tumour detection because of its ability to evaluate and classify a large amount of complex image data. Machine learning methods may appear to enhance understanding of tumour progression, but a significant amount of evidence must be obtained to use them in everyday clinical practice. The aim of this study is to review, categorise, analyse, and discuss the current developments in human tumour detection using machine learning techniques which help in cancer diagnosis and cure processes.

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Index Terms

Computer Science
Information Sciences

Keywords

Machine learning Deep learning Neural Networks Cancer disease Robotic surgery Classification Tumour detection

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