Abstract
Segmentation of medical images using deep learning has provided state-of-the-art performances in this area of work. With the availability of large digital datasets and access to powerful GPUs, deep learning has transformed our world. We are now able to make computers mimic and replicate the functions of the human mind simply by providing enough data and computing the problem. Deep learning has a huge potential for medical image analysis and now it has been firmly established as a robust tool in image segmentation. This paper addresses the six popular methods that have employed deep-learning techniques for the segmentation of medical images which play a massive impact in the medical healthcare industry and in turn make a contributing role towards the concept of smart cities. A comparative study on these deep learning-based segmentation techniques will provide a researcher working in the field of medical imaging to explore further in this area for higher accuracy and better results.
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Boro, L.O., Nandi, G. (2021). A Study on Using Deep Learning for Segmentation of Medical Image. In: Bora, P.K., Nandi, S., Laskar, S. (eds) Emerging Technologies for Smart Cities. Lecture Notes in Electrical Engineering, vol 765. Springer, Singapore. https://doi.org/10.1007/978-981-16-1550-4_14
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DOI: https://doi.org/10.1007/978-981-16-1550-4_14
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