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A novel framework of multiclass skin lesion recognition from dermoscopic images using deep learning and explainable AI
Skin cancer is a serious disease that affects people all over the world. Melanoma is an aggressive form of skin cancer, and early detection can significantly reduce human mortality. In the United States, approximately 97,610 new cases of melanoma will be diagnosed in 2023. However, challenges such a...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10281646/ https://www.ncbi.nlm.nih.gov/pubmed/37346069 http://dx.doi.org/10.3389/fonc.2023.1151257 |
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author | Ahmad, Naveed Shah, Jamal Hussain Khan, Muhammad Attique Baili, Jamel Ansari, Ghulam Jillani Tariq, Usman Kim, Ye Jin Cha, Jae-Hyuk |
author_facet | Ahmad, Naveed Shah, Jamal Hussain Khan, Muhammad Attique Baili, Jamel Ansari, Ghulam Jillani Tariq, Usman Kim, Ye Jin Cha, Jae-Hyuk |
author_sort | Ahmad, Naveed |
collection | PubMed |
description | Skin cancer is a serious disease that affects people all over the world. Melanoma is an aggressive form of skin cancer, and early detection can significantly reduce human mortality. In the United States, approximately 97,610 new cases of melanoma will be diagnosed in 2023. However, challenges such as lesion irregularities, low-contrast lesions, intraclass color similarity, redundant features, and imbalanced datasets make improved recognition accuracy using computerized techniques extremely difficult. This work presented a new framework for skin lesion recognition using data augmentation, deep learning, and explainable artificial intelligence. In the proposed framework, data augmentation is performed at the initial step to increase the dataset size, and then two pretrained deep learning models are employed. Both models have been fine-tuned and trained using deep transfer learning. Both models (Xception and ShuffleNet) utilize the global average pooling layer for deep feature extraction. The analysis of this step shows that some important information is missing; therefore, we performed the fusion. After the fusion process, the computational time was increased; therefore, we developed an improved Butterfly Optimization Algorithm. Using this algorithm, only the best features are selected and classified using machine learning classifiers. In addition, a GradCAM-based visualization is performed to analyze the important region in the image. Two publicly available datasets—ISIC2018 and HAM10000—have been utilized and obtained improved accuracy of 99.3% and 91.5%, respectively. Comparing the proposed framework accuracy with state-of-the-art methods reveals improved and less computational time. |
format | Online Article Text |
id | pubmed-10281646 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102816462023-06-21 A novel framework of multiclass skin lesion recognition from dermoscopic images using deep learning and explainable AI Ahmad, Naveed Shah, Jamal Hussain Khan, Muhammad Attique Baili, Jamel Ansari, Ghulam Jillani Tariq, Usman Kim, Ye Jin Cha, Jae-Hyuk Front Oncol Oncology Skin cancer is a serious disease that affects people all over the world. Melanoma is an aggressive form of skin cancer, and early detection can significantly reduce human mortality. In the United States, approximately 97,610 new cases of melanoma will be diagnosed in 2023. However, challenges such as lesion irregularities, low-contrast lesions, intraclass color similarity, redundant features, and imbalanced datasets make improved recognition accuracy using computerized techniques extremely difficult. This work presented a new framework for skin lesion recognition using data augmentation, deep learning, and explainable artificial intelligence. In the proposed framework, data augmentation is performed at the initial step to increase the dataset size, and then two pretrained deep learning models are employed. Both models have been fine-tuned and trained using deep transfer learning. Both models (Xception and ShuffleNet) utilize the global average pooling layer for deep feature extraction. The analysis of this step shows that some important information is missing; therefore, we performed the fusion. After the fusion process, the computational time was increased; therefore, we developed an improved Butterfly Optimization Algorithm. Using this algorithm, only the best features are selected and classified using machine learning classifiers. In addition, a GradCAM-based visualization is performed to analyze the important region in the image. Two publicly available datasets—ISIC2018 and HAM10000—have been utilized and obtained improved accuracy of 99.3% and 91.5%, respectively. Comparing the proposed framework accuracy with state-of-the-art methods reveals improved and less computational time. Frontiers Media S.A. 2023-06-06 /pmc/articles/PMC10281646/ /pubmed/37346069 http://dx.doi.org/10.3389/fonc.2023.1151257 Text en Copyright © 2023 Ahmad, Shah, Khan, Baili, Ansari, Tariq, Kim and Cha https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Ahmad, Naveed Shah, Jamal Hussain Khan, Muhammad Attique Baili, Jamel Ansari, Ghulam Jillani Tariq, Usman Kim, Ye Jin Cha, Jae-Hyuk A novel framework of multiclass skin lesion recognition from dermoscopic images using deep learning and explainable AI |
title | A novel framework of multiclass skin lesion recognition from dermoscopic images using deep learning and explainable AI |
title_full | A novel framework of multiclass skin lesion recognition from dermoscopic images using deep learning and explainable AI |
title_fullStr | A novel framework of multiclass skin lesion recognition from dermoscopic images using deep learning and explainable AI |
title_full_unstemmed | A novel framework of multiclass skin lesion recognition from dermoscopic images using deep learning and explainable AI |
title_short | A novel framework of multiclass skin lesion recognition from dermoscopic images using deep learning and explainable AI |
title_sort | novel framework of multiclass skin lesion recognition from dermoscopic images using deep learning and explainable ai |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10281646/ https://www.ncbi.nlm.nih.gov/pubmed/37346069 http://dx.doi.org/10.3389/fonc.2023.1151257 |
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