NUCEI SEGMENTATION USING U-NET
DOI:
https://doi.org/10.61841/9m52c429Keywords:
Nucleus, U-net, tumor, convolutional neural networkAbstract
Nucleus is the most important organelle in the tissue’s cell. Identifying the cell’s nuclei is the introduction for most inquiry of kidney diseases. Detection of nucleus helps to speed up the curing process for every disease. Definite and speed distinguishing of nuclei in histopathological images shows a decisive part in kidney tumor explore for detection plus individual cure. Some of the traditional findings like k-means, thresholding method are still problematic regarding pace, robust and unreliability. In previous system, post modifying of pmap was originate to comprise a large effect on nuclei recognition with low mark measures on H&E (Haemotoxylin and Eosin) stained images dataset but have a drawback in terms of processing speed. In proposed system, U-net a Convolutional Neural Network algorithm will be used on kidney tissue dataset to identify the local boundary and to segment the nuclei of kidney tissues. This process helps to predict the kidney tumours in advance and help to take health precautions.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
