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Skin Diseases Classification Using Hybrid AI Based Localization Approach
One of the most prevalent diseases that can be initially identified by visual inspection and further identified with the use of dermoscopic examination and other testing is skin cancer. Since eye observation provides the earliest opportunity for artificial intelligence to intercept various skin imag...
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/PMC9444379/ https://www.ncbi.nlm.nih.gov/pubmed/36072725 http://dx.doi.org/10.1155/2022/6138490 |
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author | Sreekala, Keshetti Rajkumar, N. Sugumar, R. Sagar, K. V. Daya Shobarani, R. Krishnamoorthy, K. Parthiban Saini, A. K. Palivela, H. Yeshitla, A. |
author_facet | Sreekala, Keshetti Rajkumar, N. Sugumar, R. Sagar, K. V. Daya Shobarani, R. Krishnamoorthy, K. Parthiban Saini, A. K. Palivela, H. Yeshitla, A. |
author_sort | Sreekala, Keshetti |
collection | PubMed |
description | One of the most prevalent diseases that can be initially identified by visual inspection and further identified with the use of dermoscopic examination and other testing is skin cancer. Since eye observation provides the earliest opportunity for artificial intelligence to intercept various skin images, some skin lesion classification algorithms based on deep learning and annotated skin photos display improved outcomes. The researcher used a variety of strategies and methods to identify and stop diseases earlier. All of them yield positive results for identifying and categorizing diseases, but proper disease categorization is still lacking. Computer-aided diagnosis is one of the most crucial methods for more accurate disease detection, although it is rarely used in dermatology. For Feature Extraction, we introduced Spectral Centroid Magnitude (SCM). The given dataset is classified using an enhanced convolutional neural network; the first stage of preprocessing uses a median filter, and the final stage compares the accuracy results to the current method. |
format | Online Article Text |
id | pubmed-9444379 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-94443792022-09-06 Skin Diseases Classification Using Hybrid AI Based Localization Approach Sreekala, Keshetti Rajkumar, N. Sugumar, R. Sagar, K. V. Daya Shobarani, R. Krishnamoorthy, K. Parthiban Saini, A. K. Palivela, H. Yeshitla, A. Comput Intell Neurosci Research Article One of the most prevalent diseases that can be initially identified by visual inspection and further identified with the use of dermoscopic examination and other testing is skin cancer. Since eye observation provides the earliest opportunity for artificial intelligence to intercept various skin images, some skin lesion classification algorithms based on deep learning and annotated skin photos display improved outcomes. The researcher used a variety of strategies and methods to identify and stop diseases earlier. All of them yield positive results for identifying and categorizing diseases, but proper disease categorization is still lacking. Computer-aided diagnosis is one of the most crucial methods for more accurate disease detection, although it is rarely used in dermatology. For Feature Extraction, we introduced Spectral Centroid Magnitude (SCM). The given dataset is classified using an enhanced convolutional neural network; the first stage of preprocessing uses a median filter, and the final stage compares the accuracy results to the current method. Hindawi 2022-08-29 /pmc/articles/PMC9444379/ /pubmed/36072725 http://dx.doi.org/10.1155/2022/6138490 Text en Copyright © 2022 Keshetti Sreekala 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 Sreekala, Keshetti Rajkumar, N. Sugumar, R. Sagar, K. V. Daya Shobarani, R. Krishnamoorthy, K. Parthiban Saini, A. K. Palivela, H. Yeshitla, A. Skin Diseases Classification Using Hybrid AI Based Localization Approach |
title | Skin Diseases Classification Using Hybrid AI Based Localization Approach |
title_full | Skin Diseases Classification Using Hybrid AI Based Localization Approach |
title_fullStr | Skin Diseases Classification Using Hybrid AI Based Localization Approach |
title_full_unstemmed | Skin Diseases Classification Using Hybrid AI Based Localization Approach |
title_short | Skin Diseases Classification Using Hybrid AI Based Localization Approach |
title_sort | skin diseases classification using hybrid ai based localization approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9444379/ https://www.ncbi.nlm.nih.gov/pubmed/36072725 http://dx.doi.org/10.1155/2022/6138490 |
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