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Machine learning with abstention for automated liver disease diagnosis

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Hamid, Kanza, Asi, Amina, Abbasi, Wajid Arshad, Sabih, Durre and Minhas, Fayyaz ul Amir Afsar (2018) Machine learning with abstention for automated liver disease diagnosis. In: 2017 International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan, 18-20 Dec 2017. Published in: 2017 International Conference on Frontiers of Information Technology (FIT) ISBN 9781538635674. doi:10.1109/FIT.2017.00070

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Official URL: https://ieeexplore.ieee.org/document/8261064

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Abstract

This paper presents a novel approach for detection of liver abnormalities in an automated manner using ultrasound images. For this purpose, we have implemented a machine learning model that can, not only generate labels (normal and abnormal) for a given ultrasound image but, it can also detect when its prediction is likely to be incorrect. The proposed model abstains from generating the label of a test example if it is not confident about its prediction. Such behavior is commonly practiced by medical doctors who, when given insufficient information or a difficult case, can choose to carry out further clinical or diagnostic tests before generating a diagnosis. However, existing machine learning models are designed in a way to always generate a label for a given example even when the confidence of their prediction is low. We have proposed a novel stochastic gradient descent based solver for the learning with abstention paradigm and use it to make a practical, state of the art method for liver disease classification. The proposed method has been benchmarked on a data set of approximately 100 patients from MINAR, Multan, Pakistan and our results show that the performance of the proposed scheme is at par with medical experts.

Item Type: Conference Item (Paper)
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
R Medicine > RC Internal medicine
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Liver -- Diseases , Liver -- Diseases -- Data processing, Machine learning , Ultrasonic imaging, Liver -- Diseases -- Imaging
Journal or Publication Title: 2017 International Conference on Frontiers of Information Technology (FIT)
Publisher: IEEE
ISBN: 9781538635674
Official Date: 18 January 2018
Dates:
DateEvent
18 January 2018Published
1 November 2017Accepted
DOI: 10.1109/FIT.2017.00070
Status: Peer Reviewed
Publication Status: Published
Reuse Statement (publisher, data, author rights): © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new coll
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 5 November 2019
Date of first compliant Open Access: 7 November 2019
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
Information Technology and Telecom Endowment FundPakistan Institute of Engineering and Applied Scienceshttp://www.pieas.edu.pk/
5000 indigenous Ph.D. scholars schemeHigher Education Commision, Pakistanhttp://dx.doi.org/10.13039/501100010221
Conference Paper Type: Paper
Title of Event: 2017 International Conference on Frontiers of Information Technology (FIT)
Type of Event: Conference
Location of Event: Islamabad, Pakistan
Date(s) of Event: 18-20 Dec 2017
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