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A Novel Approach to Skin Lesion Segmentation: Multipath Fusion Model with Fusion Loss

Segmentation of skin lesions plays a very important role in the early detection of skin cancer. However, indistinguishability due to various artifacts such as hair and contrast between normal skin and lesioned skin is an important challenge for specialist dermatologists. Computer-aided diagnostic sy...

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Autores principales: Alhudhaif, Adi, Ocal, Hakan, Barisci, Necaattin, Atacak, İsmail, Nour, Majid, Polat, Kemal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9355768/
https://www.ncbi.nlm.nih.gov/pubmed/35936380
http://dx.doi.org/10.1155/2022/2157322
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author Alhudhaif, Adi
Ocal, Hakan
Barisci, Necaattin
Atacak, İsmail
Nour, Majid
Polat, Kemal
author_facet Alhudhaif, Adi
Ocal, Hakan
Barisci, Necaattin
Atacak, İsmail
Nour, Majid
Polat, Kemal
author_sort Alhudhaif, Adi
collection PubMed
description Segmentation of skin lesions plays a very important role in the early detection of skin cancer. However, indistinguishability due to various artifacts such as hair and contrast between normal skin and lesioned skin is an important challenge for specialist dermatologists. Computer-aided diagnostic systems using deep convolutional neural networks are gaining importance in order to cope with difficulties. This study focuses on deep learning-based fusion networks and fusion loss functions. For the automatic segmentation of skin lesions, U-Net (U-Net + ResNet 2D) with 2D residual blocks and 2D volumetric convolutional neural networks were fused for the first time in this study. Also, a new fusion loss function is proposed by combining Dice Loss (DL) and Focal Tversky Loss (FTL) to make the proposed fused model more robust. Of the 2594 image dataset, 20% is reserved for test data and 80% for training data. In test data training, a Jaccard score of 0.837 and a dice score of 0.918 were obtained. The proposed model was also scored on the ISIC 2018 Task 1 test images, whose ground truths were not shared. The proposed model performed well and achieved a Jaccard index of 0.800 and a dice score of 0.880 in the ISIC 2018 Task 1 test set. In addition, it has been observed that the new fused loss function obtained by fusing Focal Tversky Loss and Dice Loss functions in the proposed model increases the robustness of the model in the tests. The proposed new loss function fusion model has outstripped the cutting-edge approaches in the literature.
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spelling pubmed-93557682022-08-06 A Novel Approach to Skin Lesion Segmentation: Multipath Fusion Model with Fusion Loss Alhudhaif, Adi Ocal, Hakan Barisci, Necaattin Atacak, İsmail Nour, Majid Polat, Kemal Comput Math Methods Med Research Article Segmentation of skin lesions plays a very important role in the early detection of skin cancer. However, indistinguishability due to various artifacts such as hair and contrast between normal skin and lesioned skin is an important challenge for specialist dermatologists. Computer-aided diagnostic systems using deep convolutional neural networks are gaining importance in order to cope with difficulties. This study focuses on deep learning-based fusion networks and fusion loss functions. For the automatic segmentation of skin lesions, U-Net (U-Net + ResNet 2D) with 2D residual blocks and 2D volumetric convolutional neural networks were fused for the first time in this study. Also, a new fusion loss function is proposed by combining Dice Loss (DL) and Focal Tversky Loss (FTL) to make the proposed fused model more robust. Of the 2594 image dataset, 20% is reserved for test data and 80% for training data. In test data training, a Jaccard score of 0.837 and a dice score of 0.918 were obtained. The proposed model was also scored on the ISIC 2018 Task 1 test images, whose ground truths were not shared. The proposed model performed well and achieved a Jaccard index of 0.800 and a dice score of 0.880 in the ISIC 2018 Task 1 test set. In addition, it has been observed that the new fused loss function obtained by fusing Focal Tversky Loss and Dice Loss functions in the proposed model increases the robustness of the model in the tests. The proposed new loss function fusion model has outstripped the cutting-edge approaches in the literature. Hindawi 2022-07-29 /pmc/articles/PMC9355768/ /pubmed/35936380 http://dx.doi.org/10.1155/2022/2157322 Text en Copyright © 2022 Adi Alhudhaif 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
Alhudhaif, Adi
Ocal, Hakan
Barisci, Necaattin
Atacak, İsmail
Nour, Majid
Polat, Kemal
A Novel Approach to Skin Lesion Segmentation: Multipath Fusion Model with Fusion Loss
title A Novel Approach to Skin Lesion Segmentation: Multipath Fusion Model with Fusion Loss
title_full A Novel Approach to Skin Lesion Segmentation: Multipath Fusion Model with Fusion Loss
title_fullStr A Novel Approach to Skin Lesion Segmentation: Multipath Fusion Model with Fusion Loss
title_full_unstemmed A Novel Approach to Skin Lesion Segmentation: Multipath Fusion Model with Fusion Loss
title_short A Novel Approach to Skin Lesion Segmentation: Multipath Fusion Model with Fusion Loss
title_sort novel approach to skin lesion segmentation: multipath fusion model with fusion loss
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9355768/
https://www.ncbi.nlm.nih.gov/pubmed/35936380
http://dx.doi.org/10.1155/2022/2157322
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