Cargando…

A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity

The complex feature characteristics and low contrast of cancer lesions, a high degree of inter-class resemblance between malignant and benign lesions, and the presence of various artifacts including hairs make automated melanoma recognition in dermoscopy images quite challenging. To date, various co...

Descripción completa

Detalles Bibliográficos
Autores principales: Montaha, Sidratul, Azam, Sami, Rafid, A. K. M. Rakibul Haque, Islam, Sayma, Ghosh, Pronab, Jonkman, Mirjam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9352099/
https://www.ncbi.nlm.nih.gov/pubmed/35925956
http://dx.doi.org/10.1371/journal.pone.0269826
_version_ 1784762579162234880
author Montaha, Sidratul
Azam, Sami
Rafid, A. K. M. Rakibul Haque
Islam, Sayma
Ghosh, Pronab
Jonkman, Mirjam
author_facet Montaha, Sidratul
Azam, Sami
Rafid, A. K. M. Rakibul Haque
Islam, Sayma
Ghosh, Pronab
Jonkman, Mirjam
author_sort Montaha, Sidratul
collection PubMed
description The complex feature characteristics and low contrast of cancer lesions, a high degree of inter-class resemblance between malignant and benign lesions, and the presence of various artifacts including hairs make automated melanoma recognition in dermoscopy images quite challenging. To date, various computer-aided solutions have been proposed to identify and classify skin cancer. In this paper, a deep learning model with a shallow architecture is proposed to classify the lesions into benign and malignant. To achieve effective training while limiting overfitting problems due to limited training data, image preprocessing and data augmentation processes are introduced. After this, the ‘box blur’ down-scaling method is employed, which adds efficiency to our study by reducing the overall training time and space complexity significantly. Our proposed shallow convolutional neural network (SCNN_12) model is trained and evaluated on the Kaggle skin cancer data ISIC archive which was augmented to 16485 images by implementing different augmentation techniques. The model was able to achieve an accuracy of 98.87% with optimizer Adam and a learning rate of 0.001. In this regard, parameter and hyper-parameters of the model are determined by performing ablation studies. To assert no occurrence of overfitting, experiments are carried out exploring k-fold cross-validation and different dataset split ratios. Furthermore, to affirm the robustness the model is evaluated on noisy data to examine the performance when the image quality gets corrupted.This research corroborates that effective training for medical image analysis, addressing training time and space complexity, is possible even with a lightweighted network using a limited amount of training data.
format Online
Article
Text
id pubmed-9352099
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-93520992022-08-05 A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity Montaha, Sidratul Azam, Sami Rafid, A. K. M. Rakibul Haque Islam, Sayma Ghosh, Pronab Jonkman, Mirjam PLoS One Research Article The complex feature characteristics and low contrast of cancer lesions, a high degree of inter-class resemblance between malignant and benign lesions, and the presence of various artifacts including hairs make automated melanoma recognition in dermoscopy images quite challenging. To date, various computer-aided solutions have been proposed to identify and classify skin cancer. In this paper, a deep learning model with a shallow architecture is proposed to classify the lesions into benign and malignant. To achieve effective training while limiting overfitting problems due to limited training data, image preprocessing and data augmentation processes are introduced. After this, the ‘box blur’ down-scaling method is employed, which adds efficiency to our study by reducing the overall training time and space complexity significantly. Our proposed shallow convolutional neural network (SCNN_12) model is trained and evaluated on the Kaggle skin cancer data ISIC archive which was augmented to 16485 images by implementing different augmentation techniques. The model was able to achieve an accuracy of 98.87% with optimizer Adam and a learning rate of 0.001. In this regard, parameter and hyper-parameters of the model are determined by performing ablation studies. To assert no occurrence of overfitting, experiments are carried out exploring k-fold cross-validation and different dataset split ratios. Furthermore, to affirm the robustness the model is evaluated on noisy data to examine the performance when the image quality gets corrupted.This research corroborates that effective training for medical image analysis, addressing training time and space complexity, is possible even with a lightweighted network using a limited amount of training data. Public Library of Science 2022-08-04 /pmc/articles/PMC9352099/ /pubmed/35925956 http://dx.doi.org/10.1371/journal.pone.0269826 Text en © 2022 Montaha et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Montaha, Sidratul
Azam, Sami
Rafid, A. K. M. Rakibul Haque
Islam, Sayma
Ghosh, Pronab
Jonkman, Mirjam
A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity
title A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity
title_full A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity
title_fullStr A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity
title_full_unstemmed A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity
title_short A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity
title_sort shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9352099/
https://www.ncbi.nlm.nih.gov/pubmed/35925956
http://dx.doi.org/10.1371/journal.pone.0269826
work_keys_str_mv AT montahasidratul ashallowdeeplearningapproachtoclassifyskincancerusingdownscalingmethodtominimizetimeandspacecomplexity
AT azamsami ashallowdeeplearningapproachtoclassifyskincancerusingdownscalingmethodtominimizetimeandspacecomplexity
AT rafidakmrakibulhaque ashallowdeeplearningapproachtoclassifyskincancerusingdownscalingmethodtominimizetimeandspacecomplexity
AT islamsayma ashallowdeeplearningapproachtoclassifyskincancerusingdownscalingmethodtominimizetimeandspacecomplexity
AT ghoshpronab ashallowdeeplearningapproachtoclassifyskincancerusingdownscalingmethodtominimizetimeandspacecomplexity
AT jonkmanmirjam ashallowdeeplearningapproachtoclassifyskincancerusingdownscalingmethodtominimizetimeandspacecomplexity
AT montahasidratul shallowdeeplearningapproachtoclassifyskincancerusingdownscalingmethodtominimizetimeandspacecomplexity
AT azamsami shallowdeeplearningapproachtoclassifyskincancerusingdownscalingmethodtominimizetimeandspacecomplexity
AT rafidakmrakibulhaque shallowdeeplearningapproachtoclassifyskincancerusingdownscalingmethodtominimizetimeandspacecomplexity
AT islamsayma shallowdeeplearningapproachtoclassifyskincancerusingdownscalingmethodtominimizetimeandspacecomplexity
AT ghoshpronab shallowdeeplearningapproachtoclassifyskincancerusingdownscalingmethodtominimizetimeandspacecomplexity
AT jonkmanmirjam shallowdeeplearningapproachtoclassifyskincancerusingdownscalingmethodtominimizetimeandspacecomplexity