DDCNN-F: double decker convolutional neural network 'F' feature fusion as a medical image classification framework.

Veeramani, N; Jayaraman, P; Krishankumar, R; Ravichandran, KS; Gandomi, AH

DDCNN-F: double decker convolutional neural network 'F' feature fusion as a medical image classification framework.

Veeramani, N Jayaraman, P Krishankumar, R Ravichandran, KS Gandomi, AH

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Publisher:: NATURE PORTFOLIO
Publication Type:: Journal Article
Citation:: Sci Rep, 2024, 14, (1), pp. 676
Issue Date:: 2024-01-05

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Field	Value	Language
dc.contributor.author	Veeramani, N
dc.contributor.author	Jayaraman, P
dc.contributor.author	Krishankumar, R
dc.contributor.author	Ravichandran, KS
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2024-07-29T08:33:44Z
dc.date.available	2023-12-11
dc.date.available	2024-07-29T08:33:44Z
dc.date.issued	2024-01-05
dc.identifier.citation	Sci Rep, 2024, 14, (1), pp. 676
dc.identifier.issn	2045-2322
dc.identifier.issn	2045-2322
dc.identifier.uri	http://hdl.handle.net/10453/179875
dc.description.abstract	Melanoma is a severe skin cancer that involves abnormal cell development. This study aims to provide a new feature fusion framework for melanoma classification that includes a novel 'F' Flag feature for early detection. This novel 'F' indicator efficiently distinguishes benign skin lesions from malignant ones known as melanoma. The article proposes an architecture that is built in a Double Decker Convolutional Neural Network called DDCNN future fusion. The network's deck one, known as a Convolutional Neural Network (CNN), finds difficult-to-classify hairy images using a confidence factor termed the intra-class variance score. These hirsute image samples are combined to form a Baseline Separated Channel (BSC). By eliminating hair and using data augmentation techniques, the BSC is ready for analysis. The network's second deck trains the pre-processed BSC and generates bottleneck features. The bottleneck features are merged with features generated from the ABCDE clinical bio indicators to promote classification accuracy. Different types of classifiers are fed to the resulting hybrid fused features with the novel 'F' Flag feature. The proposed system was trained using the ISIC 2019 and ISIC 2020 datasets to assess its performance. The empirical findings expose that the DDCNN feature fusion strategy for exposing malignant melanoma achieved a specificity of 98.4%, accuracy of 93.75%, precision of 98.56%, and Area Under Curve (AUC) value of 0.98. This study proposes a novel approach that can accurately identify and diagnose fatal skin cancer and outperform other state-of-the-art techniques, which is attributed to the DDCNN 'F' Feature fusion framework. Also, this research ascertained improvements in several classifiers when utilising the 'F' indicator, resulting in the highest specificity of + 7.34%.
dc.format	Electronic
dc.language	eng
dc.publisher	NATURE PORTFOLIO
dc.relation.ispartof	Sci Rep
dc.relation.isbasedon	10.1038/s41598-023-49721-x
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.mesh	Humans
dc.subject.mesh	Melanoma
dc.subject.mesh	Skin Neoplasms
dc.subject.mesh	Skin
dc.subject.mesh	Area Under Curve
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Skin
dc.subject.mesh	Humans
dc.subject.mesh	Melanoma
dc.subject.mesh	Skin Neoplasms
dc.subject.mesh	Area Under Curve
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Humans
dc.subject.mesh	Melanoma
dc.subject.mesh	Skin Neoplasms
dc.subject.mesh	Skin
dc.subject.mesh	Area Under Curve
dc.subject.mesh	Neural Networks, Computer
dc.title	DDCNN-F: double decker convolutional neural network 'F' feature fusion as a medical image classification framework.
dc.type	Journal Article
utslib.citation.volume	14
utslib.location.activity	England
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Data Science Institute (DSI)
pubs.organisational-group	University of Technology Sydney/Strength - DSI - Data Science Institute
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2024-07-29T08:33:35Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	14
utslib.citation.issue	1

Abstract:

Melanoma is a severe skin cancer that involves abnormal cell development. This study aims to provide a new feature fusion framework for melanoma classification that includes a novel 'F' Flag feature for early detection. This novel 'F' indicator efficiently distinguishes benign skin lesions from malignant ones known as melanoma. The article proposes an architecture that is built in a Double Decker Convolutional Neural Network called DDCNN future fusion. The network's deck one, known as a Convolutional Neural Network (CNN), finds difficult-to-classify hairy images using a confidence factor termed the intra-class variance score. These hirsute image samples are combined to form a Baseline Separated Channel (BSC). By eliminating hair and using data augmentation techniques, the BSC is ready for analysis. The network's second deck trains the pre-processed BSC and generates bottleneck features. The bottleneck features are merged with features generated from the ABCDE clinical bio indicators to promote classification accuracy. Different types of classifiers are fed to the resulting hybrid fused features with the novel 'F' Flag feature. The proposed system was trained using the ISIC 2019 and ISIC 2020 datasets to assess its performance. The empirical findings expose that the DDCNN feature fusion strategy for exposing malignant melanoma achieved a specificity of 98.4%, accuracy of 93.75%, precision of 98.56%, and Area Under Curve (AUC) value of 0.98. This study proposes a novel approach that can accurately identify and diagnose fatal skin cancer and outperform other state-of-the-art techniques, which is attributed to the DDCNN 'F' Feature fusion framework. Also, this research ascertained improvements in several classifiers when utilising the 'F' indicator, resulting in the highest specificity of + 7.34%.

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http://hdl.handle.net/10453/179875