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Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm
Hyperspectral microscopy in biology and minerals, unsupervised deep learning neural network denoising SRS photos: hyperspectral resolution enhancement and denoising one hyperspectral picture is enough to teach unsupervised method. An intuitive chemical species map for a lithium ore sample is produce...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9192273/ https://www.ncbi.nlm.nih.gov/pubmed/35800209 http://dx.doi.org/10.1155/2022/1200860 |
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author | Ajay, P. Nagaraj, B. Kumar, R. Arun Huang, Ruihang Ananthi, P. |
author_facet | Ajay, P. Nagaraj, B. Kumar, R. Arun Huang, Ruihang Ananthi, P. |
author_sort | Ajay, P. |
collection | PubMed |
description | Hyperspectral microscopy in biology and minerals, unsupervised deep learning neural network denoising SRS photos: hyperspectral resolution enhancement and denoising one hyperspectral picture is enough to teach unsupervised method. An intuitive chemical species map for a lithium ore sample is produced using k-means clustering. Many researchers are now interested in biosignals. Uncertainty limits the algorithms' capacity to evaluate these signals for further information. Even while AI systems can answer puzzles, they remain limited. Deep learning is used when machine learning is inefficient. Supervised learning needs a lot of data. Deep learning is vital in modern AI. Supervised learning requires a large labeled dataset. The selection of parameters prevents over- or underfitting. Unsupervised learning is used to overcome the challenges outlined above (performed by the clustering algorithm). To accomplish this, two processing processes were used: (1) utilizing nonlinear deep learning networks to turn data into a latent feature space (Z). The Kullback–Leibler divergence is used to test the objective function convergence. This article explores a novel research on hyperspectral microscopic picture using deep learning and effective unsupervised learning. |
format | Online Article Text |
id | pubmed-9192273 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-91922732022-07-06 Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm Ajay, P. Nagaraj, B. Kumar, R. Arun Huang, Ruihang Ananthi, P. Scanning Research Article Hyperspectral microscopy in biology and minerals, unsupervised deep learning neural network denoising SRS photos: hyperspectral resolution enhancement and denoising one hyperspectral picture is enough to teach unsupervised method. An intuitive chemical species map for a lithium ore sample is produced using k-means clustering. Many researchers are now interested in biosignals. Uncertainty limits the algorithms' capacity to evaluate these signals for further information. Even while AI systems can answer puzzles, they remain limited. Deep learning is used when machine learning is inefficient. Supervised learning needs a lot of data. Deep learning is vital in modern AI. Supervised learning requires a large labeled dataset. The selection of parameters prevents over- or underfitting. Unsupervised learning is used to overcome the challenges outlined above (performed by the clustering algorithm). To accomplish this, two processing processes were used: (1) utilizing nonlinear deep learning networks to turn data into a latent feature space (Z). The Kullback–Leibler divergence is used to test the objective function convergence. This article explores a novel research on hyperspectral microscopic picture using deep learning and effective unsupervised learning. Hindawi 2022-06-06 /pmc/articles/PMC9192273/ /pubmed/35800209 http://dx.doi.org/10.1155/2022/1200860 Text en Copyright © 2022 P. Ajay et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Ajay, P. Nagaraj, B. Kumar, R. Arun Huang, Ruihang Ananthi, P. Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm |
title | Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm |
title_full | Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm |
title_fullStr | Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm |
title_full_unstemmed | Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm |
title_short | Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm |
title_sort | unsupervised hyperspectral microscopic image segmentation using deep embedded clustering algorithm |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9192273/ https://www.ncbi.nlm.nih.gov/pubmed/35800209 http://dx.doi.org/10.1155/2022/1200860 |
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