NUCEI SEGMENTATION USING U-NET
DOI:
https://doi.org/10.61841/p05tfy93Keywords:
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.
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