Computer vision applications in healthcare: a literature review augmented with natural language processing techniques
DOI:
https://doi.org/10.33448/rsd-v11i10.32942Keywords:
Computer vision systems; Deep learning; Diagnosis; Health; Medicine.Abstract
Computer vision systems (CVS) have received special attention from researchers for their high adaptability to various contexts, especially in the security area for image and video recognition. This paper presents a literature review on the use of computer vision in healthcare over the past five years (2017-2021) and presents trends and analysis for the first six months of 2022. The Science Direct, Scopus, Web of Science, ACM Digital Library, and IEEE Xplore databases were used to conduct the search. A total of 2,072 articles were retrieved (2017 to 2021) and 492 articles in 2022 and of these, after deduplication, 1,857 papers composed the 2017-2021 corpus and 465 the 2022 corpus. Biblioshiny features (R's Bibliometrix package) were used for metrics such as journals that most publish on the topic and Natural Language Processing techniques were adopted to extract multigrams that generated word clouds from the abstracts of the retrieved articles. Brazil appears in only three papers: one by researchers from the Federal University of Acre, one from the State University of Maringa, and another from the Federal University of Santa Catarina, and all three are literature reviews. Chinese researchers appear as the most productive in the field and deep learning is the main technology adopted for this kind of study. The diseases most evidently explored in the period are breast cancer and COVID-19.
References
AIRankings. (n.d.). Retrieved July 8, 2022, from https://airankings.org/
Ballard, D. H., & Brown, C. M. (1982). Computer vision (1st ed., Vol. 1, Issue 1). Prentice-Hall, Inc. https://homepages.inf.ed.ac.uk/rbf/BOOKS/BANDB/Ballard__D._and_Brown__C._M.__1982__Computer_Vision.pdf
Berger, D. (1999). A brief history of medical diagnosis and the birth of the clinical laboratory. Part 1--Ancient times through the 19th century. MLO: Medical Laboratory Observer, 31(7).
Dawson-Howe, K. (2014). A practical introduction to computer vision with OpenCV. http://www.amazon.com/Practical-Introduction-Computer-Imaging-Technology/dp/1118848454/ref=sr_1_6?s=books&ie=UTF8&qid=1415059357&sr=1-6&keywords=opencv
de Camargo, V. P., Balancieri, R., Teixeira, H. M. P., & Guerino, G. C. (2021). Touchless Modalities of Human-Computer Interaction in Hospitals: A Systematic Literature Review. Proceedings of the XX Brazilian Symposium on Human Factors in Computing Systems. https://doi.org/10.1145/3472301.3484328
Doi, K. (2007). Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Computerized Medical Imaging and Graphics, 31(4–5), 198–211. https://doi.org/10.1016/j.compmedimag.2007.02.002
Forsyth, D. A., & Ponce, J. (2003). Computer vision: a modern approach (Vol. 1, Issue 1). Pearson Education, Inc.
Fukushima, K., & Miyake, S. (1982). Neocognitron: a self-organizing neural netwaork model for mechanism of visual pattern recognition. In S. Amari & M. A. Arbib (Eds.), Competition and cooperation neural nets (p. 19). Springer-Verlag. https://link.springer.com/book/10.1007/978-3-642-46466-9
Hubel, D. H., & Wiesel, T. N. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology, 160(1), 106–154. https://doi.org/10.1113/jphysiol.1962.sp006837
Krause, M., & Neto, M. A. C. (2021). Systematic Mapping of the Literature on Mobile Apps for People with Autistic Spectrum Disorder. Proceedings of the Brazilian Symposium on Multimedia and the Web, 45–52. https://doi.org/10.1145/3470482.3479616
Krizhevsky, B. A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. ICLR 2015.
Lane, H., Howard, C., & Hapke, H. M. (2019). Natural Language Processing in Action(Understanding,analyzing, and generating text with python).
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989). Backpropagation applied to digit recognition. In Neural computation (Vol. 1, Issue 4, pp. 541–551). https://www.ics.uci.edu/~welling/teaching/273ASpring09/lecun-89e.pdf
Matias, A. V., Amorim, J. G. A., Macarini, L. A. B., Cerentini, A., Casimiro Onofre, A. S., De Miranda Onofre, F. B., Daltoé, F. P., Stemmer, M. R., & von Wangenheim, A. (2021). What is the state of the art of computer vision-assisted cytology? A Systematic Literature Review. Computerized Medical Imaging and Graphics, 91, 101934. https://doi.org/https://doi.org/10.1016/j.compmedimag.2021.101934
Merriam-Webster Dictionary. (n.d.). Retrieved July 8, 2022, from https://www.merriam-webster.com/dictionary/diagnosis
O’Mahony, N., Campbell, S., Carvalho, A., Harapanahalli, S., Hernandez, G. V., Krpalkova, L., Riordan, D., & Walsh, J. (2020). Deep Learning vs. Traditional Computer Vision. Advances in Intelligent Systems and Computing, 943(Cv), 128–144. https://doi.org/10.1007/978-3-030-17795-9_10
Roberts, L. G. (1963). Machine perception of three-dimensional solids (Vol. 1, Issue 1) [Massachusetts Institute of Technology]. https://dspace.mit.edu/handle/1721.1/11589
Shapiro, L., & Stockman, G. (2001). Computer vision (Vol. 1, Issue 1). Pearson Education, Inc.
Shi, Z., & Govindaraju, V. (1997). Segmentation and recognition of connected handwritten numeral strings. Pattern Recognition, 30(9), 1501–1504. https://doi.org/10.1016/S0031-3203(96)00118-5
Sigel, B. (1998). A brief history of doppler ultrasound in the diagnosis of peripheral vascular disease. Ultrasound in Medicine and Biology, 24(2), 169–176. https://doi.org/10.1016/S0301-5629(97)00264-0
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104(March), 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039
Szeliski, R. (2011). Computer vision (Vol. 142). Springer-Verlag. https://doi.org/10.1007/978-3-030-79175-9_6
Top 25 countries/territories in artificial intelligence. (n.d.). Retrieved July 8, 2022, from https://www.natureindex.com/supplements/nature-index-2020-ai/tables/countries
Trucco, E., & Verri, A. (1998). Introductory techniques for 3-D computer vision (Issue 1). Prentice Hall.
Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, 1–8. https://doi.org/10.1109/cvpr.2001.990517
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Alex Sebastião Constâncio; Deborah Ribeiro Carvalho; Denise Fukumi Tsunoda
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
