Cited by 223AI in cancer detectionArtificial Intelligence in HealthcareRadiomics and Machine Learning in Medical Imaging

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A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis

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Kumar, Yogesh, Gupta, Surbhi, Singla, Ruchi, Hu, Yu-Chen·2022·Archives of Computational Methods in Engineering

DOI 10.1007/s11831-021-09648-wPMC PMC8475374

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Abstract

Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required.

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A review on recent developments in cancer detection using Machine Learning and Deep Learning models
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Authors: Kumar, Y., Gupta, S., Singla, R., & Hu, Y.-C.
Affiliations: Indus University (IN)
Providence University (TW)
Corresponding author: Kumar, Yogesh
Gupta, Surbhi
Singla, Ruchi
Hu, Yu-Chen
ORCID: Kumar, Yogesh
Gupta, Surbhi
Singla, Ruchi
Hu, Yu-Chen
Venue: Archives of Computational Methods in Engineering
Publisher: Springer Science and Business Media LLC
Published: 2021-09-27
Volume: 29
Issue: 4
Pages: 2043-2070
ISSN: 1134-3060 (ISSN-L)
1886-1784
DOI: 10.1007/s11831-021-09648-w
PMC: PMC8475374
OpenAlex: W3202138229
Cited by: 223
References: 194
Citations by year: 2026: 15 · 2025: 53 · 2024: 65 · 2023: 57 · 2022: 29 · 2021: 4
FWCI: 2.80
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Archives of Computational Methods in Engineering (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords: artificial intelligence, cancer prediction, cancer diagnosis, deep learning, machine learning classification, cancer mortality, diagnostic accuracy, medical image analysis

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A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis

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