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On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning
Skin cancer (melanoma and non-melanoma) is one of the most common cancer types and leads to hundreds of thousands of yearly deaths worldwide. It manifests itself through abnormal growth of skin cells. Early diagnosis drastically increases the chances of recovery. Moreover, it may render surgical, ra...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269808/ https://www.ncbi.nlm.nih.gov/pubmed/35808463 http://dx.doi.org/10.3390/s22134963 |
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author | Fraiwan, Mohammad Faouri, Esraa |
author_facet | Fraiwan, Mohammad Faouri, Esraa |
author_sort | Fraiwan, Mohammad |
collection | PubMed |
description | Skin cancer (melanoma and non-melanoma) is one of the most common cancer types and leads to hundreds of thousands of yearly deaths worldwide. It manifests itself through abnormal growth of skin cells. Early diagnosis drastically increases the chances of recovery. Moreover, it may render surgical, radiographic, or chemical therapies unnecessary or lessen their overall usage. Thus, healthcare costs can be reduced. The process of diagnosing skin cancer starts with dermoscopy, which inspects the general shape, size, and color characteristics of skin lesions, and suspected lesions undergo further sampling and lab tests for confirmation. Image-based diagnosis has undergone great advances recently due to the rise of deep learning artificial intelligence. The work in this paper examines the applicability of raw deep transfer learning in classifying images of skin lesions into seven possible categories. Using the HAM1000 dataset of dermoscopy images, a system that accepts these images as input without explicit feature extraction or preprocessing was developed using 13 deep transfer learning models. Extensive evaluation revealed the advantages and shortcomings of such a method. Although some cancer types were correctly classified with high accuracy, the imbalance of the dataset, the small number of images in some categories, and the large number of classes reduced the best overall accuracy to 82.9%. |
format | Online Article Text |
id | pubmed-9269808 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92698082022-07-09 On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning Fraiwan, Mohammad Faouri, Esraa Sensors (Basel) Article Skin cancer (melanoma and non-melanoma) is one of the most common cancer types and leads to hundreds of thousands of yearly deaths worldwide. It manifests itself through abnormal growth of skin cells. Early diagnosis drastically increases the chances of recovery. Moreover, it may render surgical, radiographic, or chemical therapies unnecessary or lessen their overall usage. Thus, healthcare costs can be reduced. The process of diagnosing skin cancer starts with dermoscopy, which inspects the general shape, size, and color characteristics of skin lesions, and suspected lesions undergo further sampling and lab tests for confirmation. Image-based diagnosis has undergone great advances recently due to the rise of deep learning artificial intelligence. The work in this paper examines the applicability of raw deep transfer learning in classifying images of skin lesions into seven possible categories. Using the HAM1000 dataset of dermoscopy images, a system that accepts these images as input without explicit feature extraction or preprocessing was developed using 13 deep transfer learning models. Extensive evaluation revealed the advantages and shortcomings of such a method. Although some cancer types were correctly classified with high accuracy, the imbalance of the dataset, the small number of images in some categories, and the large number of classes reduced the best overall accuracy to 82.9%. MDPI 2022-06-30 /pmc/articles/PMC9269808/ /pubmed/35808463 http://dx.doi.org/10.3390/s22134963 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Fraiwan, Mohammad Faouri, Esraa On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning |
title | On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning |
title_full | On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning |
title_fullStr | On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning |
title_full_unstemmed | On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning |
title_short | On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning |
title_sort | on the automatic detection and classification of skin cancer using deep transfer learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269808/ https://www.ncbi.nlm.nih.gov/pubmed/35808463 http://dx.doi.org/10.3390/s22134963 |
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