Computer vision applications in healthcare: a literature review augmented with natural language processing techniques




Computer vision systems; Deep learning; Diagnosis; Health; Medicine.


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.


AIRankings. (n.d.). Retrieved July 8, 2022, from

Ballard, D. H., & Brown, C. M. (1982). Computer vision (1st ed., Vol. 1, Issue 1). Prentice-Hall, Inc.

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.

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.

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.

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.

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.

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.

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).

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.

Merriam-Webster Dictionary. (n.d.). Retrieved July 8, 2022, from

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.

Roberts, L. G. (1963). Machine perception of three-dimensional solids (Vol. 1, Issue 1) [Massachusetts Institute of Technology].

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.

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.

Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104(March), 333–339.

Szeliski, R. (2011). Computer vision (Vol. 142). Springer-Verlag.

Top 25 countries/territories in artificial intelligence. (n.d.). Retrieved July 8, 2022, from

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.



How to Cite

CONSTÂNCIO, A. S.; CARVALHO, D. R.; TSUNODA, D. F. Computer vision applications in healthcare: a literature review augmented with natural language processing techniques. Research, Society and Development, [S. l.], v. 11, n. 10, p. e218111032942, 2022. DOI: 10.33448/rsd-v11i10.32942. Disponível em: Acesso em: 4 dec. 2023.



Review Article