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Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks
In the automatic detection framework, there have been many attempts to develop models for real-time melanoma detection. To effectively discriminate benign and malign skin lesions, this work investigates sixty different architectures of the Feedforward Back Propagation Network (FFBPN), based on shape...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8224667/ https://www.ncbi.nlm.nih.gov/pubmed/34067493 http://dx.doi.org/10.3390/diagnostics11060936 |
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author | Moldovanu, Simona Obreja, Cristian-Dragos Biswas, Keka C. Moraru, Luminita |
author_facet | Moldovanu, Simona Obreja, Cristian-Dragos Biswas, Keka C. Moraru, Luminita |
author_sort | Moldovanu, Simona |
collection | PubMed |
description | In the automatic detection framework, there have been many attempts to develop models for real-time melanoma detection. To effectively discriminate benign and malign skin lesions, this work investigates sixty different architectures of the Feedforward Back Propagation Network (FFBPN), based on shape asymmetry for an optimal structural design that includes both the hidden neuron number and the input data selection. The reason for the choice of shape asymmetry was based on the 5–10% disagreement between dermatologists regarding the efficacy of asymmetry in the diagnosis of malignant melanoma. Asymmetry is quantified based on lesion shape (contour), moment of inertia of the lesion shape and histograms. The FFBPN has a high architecture flexibility, which indicates it as a favorable tool to avoid the over-parameterization of the ANN and, equally, to discard those redundant input datasets that usually result in poor test performance. The FFBPN was tested on four public image datasets containing melanoma, dysplastic nevus and nevus images. Experimental results on multiple benchmark data sets demonstrate that asymmetry A2 is a meaningful feature for skin lesion classification, and FFBPN with 16 neurons in the hidden layer can model the data without compromising prediction accuracy. |
format | Online Article Text |
id | pubmed-8224667 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82246672021-06-25 Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks Moldovanu, Simona Obreja, Cristian-Dragos Biswas, Keka C. Moraru, Luminita Diagnostics (Basel) Article In the automatic detection framework, there have been many attempts to develop models for real-time melanoma detection. To effectively discriminate benign and malign skin lesions, this work investigates sixty different architectures of the Feedforward Back Propagation Network (FFBPN), based on shape asymmetry for an optimal structural design that includes both the hidden neuron number and the input data selection. The reason for the choice of shape asymmetry was based on the 5–10% disagreement between dermatologists regarding the efficacy of asymmetry in the diagnosis of malignant melanoma. Asymmetry is quantified based on lesion shape (contour), moment of inertia of the lesion shape and histograms. The FFBPN has a high architecture flexibility, which indicates it as a favorable tool to avoid the over-parameterization of the ANN and, equally, to discard those redundant input datasets that usually result in poor test performance. The FFBPN was tested on four public image datasets containing melanoma, dysplastic nevus and nevus images. Experimental results on multiple benchmark data sets demonstrate that asymmetry A2 is a meaningful feature for skin lesion classification, and FFBPN with 16 neurons in the hidden layer can model the data without compromising prediction accuracy. MDPI 2021-05-22 /pmc/articles/PMC8224667/ /pubmed/34067493 http://dx.doi.org/10.3390/diagnostics11060936 Text en © 2021 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 Moldovanu, Simona Obreja, Cristian-Dragos Biswas, Keka C. Moraru, Luminita Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks |
title | Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks |
title_full | Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks |
title_fullStr | Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks |
title_full_unstemmed | Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks |
title_short | Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks |
title_sort | towards accurate diagnosis of skin lesions using feedforward back propagation neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8224667/ https://www.ncbi.nlm.nih.gov/pubmed/34067493 http://dx.doi.org/10.3390/diagnostics11060936 |
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