Cargando…
Classification of skin cancer from dermoscopic images using deep neural network architectures
A powerful medical decision support system for classifying skin lesions from dermoscopic images is an important tool to prognosis of skin cancer. In the recent years, Deep Convolutional Neural Network (DCNN) have made a significant advancement in detecting skin cancer types from dermoscopic images,...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9554840/ https://www.ncbi.nlm.nih.gov/pubmed/36250184 http://dx.doi.org/10.1007/s11042-022-13847-3 |
_version_ | 1784806787061383168 |
---|---|
author | S M, Jaisakthi P, Mirunalini Aravindan, Chandrabose Appavu, Rajagopal |
author_facet | S M, Jaisakthi P, Mirunalini Aravindan, Chandrabose Appavu, Rajagopal |
author_sort | S M, Jaisakthi |
collection | PubMed |
description | A powerful medical decision support system for classifying skin lesions from dermoscopic images is an important tool to prognosis of skin cancer. In the recent years, Deep Convolutional Neural Network (DCNN) have made a significant advancement in detecting skin cancer types from dermoscopic images, in-spite of its fine grained variability in its appearance. The main objective of this research work is to develop a DCNN based model to automatically classify skin cancer types into melanoma and non-melanoma with high accuracy. The datasets used in this work were obtained from the popular challenges ISIC-2019 and ISIC-2020, which have different image resolutions and class imbalance problems. To address these two problems and to achieve high performance in classification we have used EfficientNet architecture based on transfer learning techniques, which learns more complex and fine grained patterns from lesion images by automatically scaling depth, width and resolution of the network. We have augmented our dataset to overcome the class imbalance problem and also used metadata information to improve the classification results. Further to improve the efficiency of the EfficientNet we have used ranger optimizer which considerably reduces the hyper parameter tuning, which is required to achieve state-of-the-art results. We have conducted several experiments using different transferring models and our results proved that EfficientNet variants outperformed in the skin lesion classification tasks when compared with other architectures. The performance of the proposed system was evaluated using Area under the ROC curve (AUC - ROC) and obtained the score of 0.9681 by optimal fine tuning of EfficientNet-B6 with ranger optimizer. |
format | Online Article Text |
id | pubmed-9554840 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-95548402022-10-12 Classification of skin cancer from dermoscopic images using deep neural network architectures S M, Jaisakthi P, Mirunalini Aravindan, Chandrabose Appavu, Rajagopal Multimed Tools Appl Track 2: Medical Applications of Multimedia A powerful medical decision support system for classifying skin lesions from dermoscopic images is an important tool to prognosis of skin cancer. In the recent years, Deep Convolutional Neural Network (DCNN) have made a significant advancement in detecting skin cancer types from dermoscopic images, in-spite of its fine grained variability in its appearance. The main objective of this research work is to develop a DCNN based model to automatically classify skin cancer types into melanoma and non-melanoma with high accuracy. The datasets used in this work were obtained from the popular challenges ISIC-2019 and ISIC-2020, which have different image resolutions and class imbalance problems. To address these two problems and to achieve high performance in classification we have used EfficientNet architecture based on transfer learning techniques, which learns more complex and fine grained patterns from lesion images by automatically scaling depth, width and resolution of the network. We have augmented our dataset to overcome the class imbalance problem and also used metadata information to improve the classification results. Further to improve the efficiency of the EfficientNet we have used ranger optimizer which considerably reduces the hyper parameter tuning, which is required to achieve state-of-the-art results. We have conducted several experiments using different transferring models and our results proved that EfficientNet variants outperformed in the skin lesion classification tasks when compared with other architectures. The performance of the proposed system was evaluated using Area under the ROC curve (AUC - ROC) and obtained the score of 0.9681 by optimal fine tuning of EfficientNet-B6 with ranger optimizer. Springer US 2022-10-12 2023 /pmc/articles/PMC9554840/ /pubmed/36250184 http://dx.doi.org/10.1007/s11042-022-13847-3 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Track 2: Medical Applications of Multimedia S M, Jaisakthi P, Mirunalini Aravindan, Chandrabose Appavu, Rajagopal Classification of skin cancer from dermoscopic images using deep neural network architectures |
title | Classification of skin cancer from dermoscopic images using deep neural network architectures |
title_full | Classification of skin cancer from dermoscopic images using deep neural network architectures |
title_fullStr | Classification of skin cancer from dermoscopic images using deep neural network architectures |
title_full_unstemmed | Classification of skin cancer from dermoscopic images using deep neural network architectures |
title_short | Classification of skin cancer from dermoscopic images using deep neural network architectures |
title_sort | classification of skin cancer from dermoscopic images using deep neural network architectures |
topic | Track 2: Medical Applications of Multimedia |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9554840/ https://www.ncbi.nlm.nih.gov/pubmed/36250184 http://dx.doi.org/10.1007/s11042-022-13847-3 |
work_keys_str_mv | AT smjaisakthi classificationofskincancerfromdermoscopicimagesusingdeepneuralnetworkarchitectures AT pmirunalini classificationofskincancerfromdermoscopicimagesusingdeepneuralnetworkarchitectures AT aravindanchandrabose classificationofskincancerfromdermoscopicimagesusingdeepneuralnetworkarchitectures AT appavurajagopal classificationofskincancerfromdermoscopicimagesusingdeepneuralnetworkarchitectures |