Brain tumour segmentation is one of the most significant tasks in medical image processing. It is believed that early diagnosis of brain tumours is essential for enhancing treatment options and raising patient survival rates. The manual segmentation is dependent on radiotherapist involvement and expertise. MRI scans are often speedy and an excellent diagnostic tool for medical professionals. As a result, in an emergency, doctors advise getting an MRI scan. However, there is a chance for inaccuracy because there is a lot of MRI data. This has made automatic brain tumor segmentation a feasible process. Currently, machine learning methods are in use for segmentation. This research proposes segmentation of brain tumour using modified LinkNet architecture from MRI images.
@article{ruba2023,
author = {T. Ruba and Dr. R. Tamilselvi and Dr. M. Parisa Beham and M. Gayathri},
title = {{Segmentation of a Brain Tumour using Modified LinkNet Architecture from MRI Images}},
journal = {Journal of Innovative Image Processing},
volume = {5},
number = {2},
pages = {161-180},
year = {2023},
publisher = {IRO Journals},
doi = {10.36548/jiip.2023.2.007},
url = {https://doi.org/10.36548/jiip.2023.2.007}
}
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