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Study on Medical Image Detection Using Deep Learning

Study on Medical Image Detection Using Deep Learning

Ruchi Garg, Sushil Kumar, Sonali Gupta
Copyright: © 2022 |Pages: 17
ISBN13: 9781668425084|ISBN10: 1668425084|EISBN13: 9781668425091
DOI: 10.4018/978-1-6684-2508-4.ch001
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MLA

Garg, Ruchi, et al. "Study on Medical Image Detection Using Deep Learning." Smart Healthcare for Sustainable Urban Development, edited by Rosy Madaan, et al., IGI Global, 2022, pp. 1-17. https://doi.org/10.4018/978-1-6684-2508-4.ch001

APA

Garg, R., Kumar, S., & Gupta, S. (2022). Study on Medical Image Detection Using Deep Learning. In R. Madaan, K. Bhatia, S. Panda, V. Jain, & P. Gulati (Eds.), Smart Healthcare for Sustainable Urban Development (pp. 1-17). IGI Global. https://doi.org/10.4018/978-1-6684-2508-4.ch001

Chicago

Garg, Ruchi, Sushil Kumar, and Sonali Gupta. "Study on Medical Image Detection Using Deep Learning." In Smart Healthcare for Sustainable Urban Development, edited by Rosy Madaan, et al., 1-17. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-2508-4.ch001

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Abstract

The growing development of deep learning and its increasingly widespread use signals a trend to convert to a new standard in each application area, consolidating itself as a general purpose technology. In the general analysis of innovations based on the use of deep learning on different applications of medical images, it can be inferred that its use is increasingly widespread and that it is driving greater innovations and transformations at an increasingly greater speed. Additionally, it is evident that the use of deep learning in medical imaging for the diagnosis of various anomalies incorporates a significant number of the elements and methodological and organizational aspects identified under this study (e.g., invention methods from the perspective of application of deep learning). Therefore, this study emphasizes various aspects of deep learning models to have opted as a ready reference while examining the medical images as a de-facto standard.

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