Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects
Date
2020Author
Albahri, O.S.
Zaidan, A.A.
Albahri, A.S.
Zaidan, B.B.
Abdulkareem, Karrar Hameed
Al-qaysi, Z.T.
Alamoodi, A.H.
Aleesa, A.M.
Chyad, M.A.
Alesa, R.M.
Kem, L.C.
Modi Lakulu, Muhammad
Ibrahim, A.B.
Abdul Rashid, Nazre
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Abstract
This study presents a systematic review of artificial intelligence (AI) techniques used in the detection
and classification of coronavirus disease 2019 (COVID-19) medical images in terms of evaluation and
benchmarking. Five reliable databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and
Scopus were used to obtain relevant studies of the given topic. Several filtering and scanning stages were
performed according to the inclusion/exclusion criteria to screen the 36 studies obtained; however, only
11 studies met the criteria. Taxonomy was performed, and the 11 studies were classified on the basis
of two categories, namely, review and research studies. Then, a deep analysis and critical review were
performed to highlight the challenges and critical gaps outlined in the academic literature of the given
subject. Results showed that no relevant study evaluated and benchmarked AI techniques utilised in
classification tasks (i.e. binary, multi-class, multi-labelled and hierarchical classifications) of COVID-19
medical images. In case evaluation and benchmarking will be conducted, three future challenges will
be encountered, namely, multiple evaluation criteria within each classification task, trade-off amongst
criteria and importance of these criteria. According to the discussed future challenges, the process of
evaluation and benchmarking AI techniques used in the classification of COVID-19 medical images considered multi-complex attribute problems. Thus, adopting multi-criteria decision analysis (MCDA) is
an essential and effective approach to tackle the problem complexity. Moreover, this study proposes a
detailed methodology for the evaluation and benchmarking ofAItechniques used in all classification tasks
of COVID-19 medical images as future directions; such methodology is presented on the basis of three
sequential phases. Firstly, the identification procedure for the construction of four decision matrices,
namely, binary, multi-class, multi-labelled and hierarchical, is presented on the basis of the intersection
of evaluation criteria of each classification task and AI classification techniques. Secondly, the development of the MCDA approach for benchmarking AI classification techniques is provided on the basis of
the integrated analytic hierarchy process and VlseKriterijumska Optimizacija I Kompromisno Resenje
methods. Lastly, objective and subjective validation procedures are described to validate the proposed
benchmarking solutions
Palabras clave
COVID-19; Medical image; Artificial intelligence; Evaluation; Benchmarking; Decision-making; MCDALink to resource
https://doi.org/10.1016/j.jiph.2020.06.028Collections
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