The DOI matches its Crossref record; 197 of the 197 references it cites were found in a registry or an index.
A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis
Open accessAbstract
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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“Research by Kumar et al. (76) and Zhang et al. (77) examined the use of AI algorithms in cancer and diagnosis. The studies revealed that AI algorithms achieved high accuracy rates in diagnosing cancer subtypes, assisting clinicians in developing tailored treatment plans.”
From Improving health literacy using the power of digital communications to achieve better health outcomes for patients and practitioners. (2023) · cites this paper as [76]
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Cites
Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008
Ferlay, J., Shin, H., Bray, F., Forman, D., Mathers, C. D., & Parkin, D. M. (2010) · International Journal of Cancer · doi:10.1002/ijc.25516 · Cited by 21,468
Ror2 signaling regulates Golgi structure and transport through IFT20 for tumor invasiveness
Nishita, M., Park, S.-Y., Nishio, T., Kamizaki, K., Wang, Z., Tamada, K., Takumi, T., Hashimoto, R., Otani, H., Pazour, G. J., et al. (2017) · Scientific Reports · doi:10.1038/s41598-016-0028-x · Cited by 15,947
Abiraterone and Increased Survival in Metastatic Prostate Cancer
de Bono, J. S., Logothetis, C. J., Molina, A., Fizazi, K., North, S., Chu, L., Chi, K. N., Jones, R. J., Goodman, O. B., Saad, F., et al. (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1014618 · Cited by 4,407 · cited 2×
“Most of the research works were published in the years 2020 (35), 2019 (32), 2018 (30).”
BackgroundMortality Results from a Randomized Prostate-Cancer Screening Trial
Andriole, G. L., Crawford, E. D., Grubb, R. L., Buys, S. S., Chia, D., Church, T. R., Fouad, M. N., Gelmann, E. P., Kvale, P. A., Reding, D. J., et al. (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0810696 · Cited by 2,922
Colorectal Cancer Epidemiology: Incidence, Mortality, Survival, and Risk Factors
Haggar, F. A., & Boushey, R. P. (2009) · Clinics in Colon and Rectal Surgery · doi:10.1055/s-0029-1242458 · Cited by 2,018
Review: Ultraviolet radiation and skin cancer
Narayanan, D. L., Saladi, R. N., & FOX, J. L. (2010) · International Journal of Dermatology · doi:10.1111/j.1365-4632.2010.04474.x · Cited by 1,269
Benefits and Harms of CT Screening for Lung Cancer
Bach, P. B., Mirkin, J. N., Oliver, T. K., Azzoli, C. G., Berry, D. A., Brawley, O. W., Byers, T., Colditz, G. A., Gould, M. K., Jett, J. R., et al. (2012) · JAMA · doi:10.1001/jama.2012.5521 · Cited by 1,237
Lee, L. Y., Cazier, J.-B., Angelis, V., Arnold, R., Bisht, V., Campton, N. A., Chackathayil, J., Cheng, V. W., Curley, H. M., Fittall, M. W., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)31173-9 · Cited by 1,103
Classification using deep learning neural networks for brain tumors
Mohsen, H., El‐Dahshan, E. A., El-Horbaty, E.-S. M., & Salem, A.-B. M. (2017) · Future Computing and Informatics Journal · doi:10.1016/j.fcij.2017.12.001 · Cited by 1,076
Epidemiological characteristics of and risk factors for breast cancer in the world
Momenimovahed, Z., & Salehiniya, H. (2019) · Breast Cancer Targets and Therapy · doi:10.2147/bctt.s176070 · Cited by 1,059
Research works printed but not linked: 11
- Gupta R, Sarwar A, Sharma V (2017) Screening of cervical cancer by artificial intelligence based analysis of digitized papanicolaou-smear images. Int J Contemp Med Res 4(5):1–6
- Lavanya L, Chandra J (2019) Oral cancer analysis using machine learning techniques. Int J Eng Res Technol 12(5):596–601
- Nguyen Q, Lee J, Huang M, Khullar A, Raymond P (2015) Diagnosis and treatment of patient with thyroid cancer. Clinical 1:1–40
- Ning Y, Yu Z, Pan Y (2017) A deep learning method for lincRNA detection using auto-encoder algorithm. BMC Bioinformatics 18:1–9
- Nirupama T, Damodhar J (2016) A GSM based computer aided diagnosis system for lung cancer detection. In: National conference on emerging trends in information, digital and embedded systems, 137–142
- Nivaashini M, Soundariya R (2018) Deep Boltzmann machine based breast cancer risk detection for healthcare systems. Int J Pure Appl Math 119:581–590
- Sajenna TA, Jereesh AS (2015) Automated cervical cancer detection through rgvf segmentation and SVM classification. Int Conf Comput Network Commun Trivandrum 2015:663–669
- Saraf J, Kalpana V (2017) Thyroid cancer detection using image processing. Int J Res Sci Innov 4(8):75–77
- Sasikala S, Bharathi M, Sowmiya B (2018) Lung Cancer detection and classification using deep CNN. Int J Innov Technol Explor Eng 8:259–262
- Selvathi D, Poornila A (2017) Breast cancer detection in mammogram images using deep learning technique. J Sci Res 25(2):417–426. https://doi.org/10.5829/idosi.mejsr.2017.417.426 · DOI
- Sokoutil M, Sokouti M, Sokouti B (2018) Computer aided diagnosis of thyroid cancer using image processing techniques. Int J Comput Sci Network Secur 18(4):1–8
Cited by
Sorted by citations
Fitzpatrick, P. J. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1264780 · Cited by 416
Zhang, B., Shi, H., & Wang, H. (2023) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s410301 · Cited by 347
Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions
Sebastian, A. M., & Peter, D. (2022) · Life · doi:10.3390/life12121991 · Cited by 199
Deep learning techniques for cancer classification using microarray gene expression data
Gupta, S., Gupta, M. K., Shabaz, M., & Sharma, A. K. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.952709 · Cited by 110
Self-citationA review on recent developments in cancer detection using Machine Learning and Deep Learning models
Maurya, S., Tiwari, S., Mothukuri, M. C., Tangeda, C. M., Nandigam, R. N. S., & Addagiri, D. C. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104398 · Cited by 98
Metadata
- 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
- Venue
- Archives of Computational Methods in Engineering
- Published
- 2021-09-27
- Volume
- 29
- Issue
- 4
- Pages
- 2043-2070
- ISSN
- 1134-3060 (ISSN-L)
- 1886-1784
- 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
- 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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