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2-HDCNN: A two-tier hybrid dual convolution neural network feature fusion approach for diagnosing malignant melanoma

Computers in biology and medicine, 2023-01, Vol.152, p.106333-106333, Article 106333 [Peer Reviewed Journal]

2022 ;Copyright © 2022. Published by Elsevier Ltd. ;Copyright Elsevier Limited Jan 2023 ;ISSN: 0010-4825 ;EISSN: 1879-0534 ;DOI: 10.1016/j.compbiomed.2022.106333 ;PMID: 36463793

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Title:
2-HDCNN: A two-tier hybrid dual convolution neural network feature fusion approach for diagnosing malignant melanoma
Author: Nancy Jane, Y. ; Charanya, S.K. ; Amsaprabhaa, M. ; Jayashanker, Preetiha ; Nehemiah H., Khanna
Subjects: ABCD ; Artificial neural networks ; Asymmetry ; Automation ; CAD ; Classification ; Classifiers ; CNN ; Convolution ; Data augmentation, Gradient boosting classifier ; Datasets ; Decision trees ; Deep learning ; Disease ; Hair ; Hair removal ; Humans ; Information technology ; Medical diagnosis ; Melanoma ; Melanoma - diagnostic imaging ; Melanoma - pathology ; Melanoma, Cutaneous Malignant ; Multilayer perceptrons ; Multilayers ; Neural networks ; Neural Networks, Computer ; Performance assessment ; Skin cancer ; Skin Diseases ; Skin Neoplasms - diagnostic imaging ; Skin Neoplasms - genetics ; Statistical analysis ; Support vector machines
Is Part Of: Computers in biology and medicine, 2023-01, Vol.152, p.106333-106333, Article 106333
Description: Melanoma is a fatal form of skin cancer, which causes excess skin cell growth in the body. The objective of this work is to develop a two-tier hybrid dual convolution neural network (2-HDCNN) feature fusion approach for malignant melanoma prediction. The first-tier baseline Convolutional Neural Network (CNN) extracts the hard to classify samples based on the confidence factor (class probability variance score) and generates a Baseline Segregated Dataset (BSD). The BSD is then preprocessed using hair removal and data augmentation techniques. The preprocessed BSD is trained with the second-tier CNN that yields the bottleneck features. These features are then combined with the derived features from the ABCD (Asymmetry, Border, Color and Diameter) medical rule to improve classification accuracy. The generated hybrid fused features are fed to different classifiers like Gradient boosting classifiers, Bagging classifiers, XGBoost classifiers, Decision trees, Support Vector Machine, Logistic regression and Multi-layer perceptron. For performance assessment, the proposed framework is trained on the ISIC 2018 dataset. The experimental results prove that the presented 2-HDCNN feature fusion approach has reached an accuracy of 92.15%, precision of 96.96%, specificity of 96.8%, sensitivity of 86.48%, and AUC (Area Under Curve) value of 0.96 for diagnosing malignant melanoma. •Develop 2-HDCNN feature selection framework for malignant melanoma prediction.•2-HDCNN combines the dual CNN and ABCD attributes for hybrid feature selection.•Classification model is built using selected hybrid features.•Experimental results have proven the efficiency of the proposed 2-HDCNN approach.
Publisher: United States: Elsevier Ltd
Language: English
Identifier: ISSN: 0010-4825
EISSN: 1879-0534
DOI: 10.1016/j.compbiomed.2022.106333
PMID: 36463793
Source: MEDLINE
ProQuest Central

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2-HDCNN: A two-tier hybrid dual convolution neural network feature fusion approach for diagnosing malignant melanoma

2022 ;Copyright © 2022. Published by Elsevier Ltd. ;Copyright Elsevier Limited Jan 2023 ;ISSN: 0010-4825 ;EISSN: 1879-0534 ;DOI: 10.1016/j.compbiomed.2022.106333 ;PMID: 36463793

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