Open Access
REVIEW
Importance of Features Selection, Attributes Selection, Challenges and Future Directions for Medical Imaging Data: A Review
Nazish Naheed1, Muhammad Shaheen1, Sajid Ali Khan1, Mohammed Alawairdhi2,*, Muhammad Attique Khan3,*
1 Faculty of Engineering & Information Technology, Foundation University Islamabad, Islamabad, Pakistan
2 College of Computing and Informatics, Saudi Electronic University, Rayadh, Saudi Arabia
3 Department of Computer Science, HITEC University, Taxila, Pakistan
* Corresponding Authors: Mohammed Alawairdhi. Email: ; Muhammad Attique Khan. Email:
(This article belongs to this Special Issue: Recent Advances on Deep Learning for Medical Signal Analysis (RADLMSA))
Computer Modeling in Engineering & Sciences 2020, 125(1), 315-344. https://doi.org/10.32604/cmes.2020.011380
Received 04 July 2020; Accepted 17 July 2020; Issue published 18 September 2020
Abstract
In the area of pattern recognition and machine learning, features
play a key role in prediction. The famous applications of features are medical
imaging, image classification, and name a few more. With the exponential
growth of information investments in medical data repositories and health
service provision, medical institutions are collecting large volumes of data.
These data repositories contain details information essential to support medical diagnostic decisions and also improve patient care quality. On the other
hand, this growth also made it difficult to comprehend and utilize data for
various purposes. The results of imaging data can become biased because of
extraneous features present in larger datasets. Feature selection gives a chance
to decrease the number of components in such large datasets. Through selection techniques, ousting the unimportant features and selecting a subset of
components that produces prevalent characterization precision. The correct
decision to find a good attribute produces a precise grouping model, which
enhances learning pace and forecast control. This paper presents a review
of feature selection techniques and attributes selection measures for medical
imaging. This review is meant to describe feature selection techniques in a
medical domain with their pros and cons and to signify its application in imaging data and data mining algorithms. The review reveals the shortcomings of
the existing feature and attributes selection techniques to multi-sourced data.
Moreover, this review provides the importance of feature selection for correct
classification of medical infections. In the end, critical analysis and future
directions are provided.
Keywords
Cite This Article
Naheed, N., Shaheen, M., Khan, S. A., Alawairdhi, M., Khan, M. A. (2020). Importance of Features Selection, Attributes Selection, Challenges and Future Directions for Medical Imaging Data: A Review.
CMES-Computer Modeling in Engineering & Sciences, 125(1), 315–344.
Citations