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Sustainable and Reliable Healthcare Automation and Digitization using Machine Learning Techniques

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Field Value
 
Title Sustainable and Reliable Healthcare Automation and Digitization using Machine Learning Techniques
 
Creator Sekhar, B V D S
Raju, Bh V S Ramakrishnam
Kumar, N Udaya
Chakravarthy, VVSSS
 
Subject Brain tumor
Deep learning
Healthcare 4.0
Industry 4.0
Sustainable technology
 
Description 226-231
Healthcare 4.0 takes significant benefits while aligned with Industry 4.0. Mainly citing the recent and existing pandemic,
the need for Industry Internet of Things (IIoT), automation, digitalization, and induction of machine learning techniques for
forecasting and prediction have been the technologies to rely on. On these lines, digitization and automation in the
healthcare industry have been practical tools to accelerate diagnosis and provide handy second opinions to practitioners.
Sustainability in health care has several objectives, like reduced cost and low emission rate, while promising effective
outcomes and ease of diagnosis. In this paper, such an attempt has been made to employ deep learning techniques to predict
the phase of brain tumors. The deep learning methods help practitioners to correlate patients' status with similar subjects and
assess and predict future anomalies due to brain tumors. Popular datasets have been employed for modeling the prediction
process. Machine learning has been the most successful tool for handling supervised classification while dealing with
complex patterns. The study aims to apply this machine learning technique to classifying images of brains with different
types of tumors: meningioma, glioma, and pituitary. The simulation is performed in a python environment, and analysis is
carried out using standard metrics.
 
Date 2023-02-08T05:19:23Z
2023-02-08T05:19:23Z
2023-02
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/61362
https://doi.org/10.56042/jsir.v82i2.70222
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.82(02) [February 2023]