Improving the performance of computer-aided diagnosis systems using semi-supervised learning: a survey and analysis
by Asma Chebli; Akila Djebbar; Hayet Farida Djellali Merouani
International Journal of Intelligent Information and Database Systems (IJIIDS), Vol. 13, No. 2/3/4, 2020

Abstract: The healthcare sector generates important amount of medical data on a daily basis, several machine learning (ML) methods have been developed and studied in order to usefully exploit this substantial sum of information generated colossally, in a wide range practical data mining applications. Yet, an essential key when it comes to developing a competent computer-aided diagnosis (CAD) system is the supervision of data, made by expert annotators; a labelling process considered as a challenging task; as it is both very time consuming and expensive. This survey paper provides the influence of semi-supervised learning framework as it addresses the scarcity of the supervised data for the development of computer-aided diagnosis systems. The methods used and results obtained are discussed and key findings are highlighted. Further, in the light of this review some directions for future research are given; we present a proposed approach using a semi-supervised technique as a core for the learning of a case-based reasoning (CBR) system in CAD context.

Online publication date: Wed, 09-Sep-2020

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Information and Database Systems (IJIIDS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com