Open AccessWavelet‐based deep learning for skin lesion classification
Author(s) -
Serte Sertan,
Demirel Hasan
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.0553
Subject(s) - wavelet , artificial intelligence , skin cancer , pattern recognition (psychology) , transformation (genetics) , skin lesion , computer science , deep learning , wavelet transform , lesion , contextual image classification , image (mathematics) , dermatology , cancer , medicine , pathology , biochemistry , chemistry , gene
Skin lesions can be in malignant or benign forms. Benign skin lesion types are not deadly; however, malignant types of skin lesions can be fatal. Lethal forms are known as skin cancer. These types require urgent clinical treatment. Fast detection and diagnosis of malignant types of skin lesions might prevent life‐threatening scenarios. This work presents two methods for the automatic classification of malignant melanoma and seborrhoeic keratosis lesions. The first method builds on modelling skin images together with wavelet coefficients. Approximate, horizontal, and vertical wavelet coefficients are obtained using the wavelet transform, and then deep learning (DL) models are generated for each of the representations and skin images. The second method builds on modelling skin images together with three approximate coefficients. This method utilises a sequential wavelet transformation to produce approximation coefficients. Then DL models are generated for each of the representations and skin images. Transfer learning‐based ResNet‐18 and ResNet‐50 DL models provide model images and wavelet coefficients. Then skin lesion detection is achieved by fusing model output probabilities. Both proposed models outperform the methods only based on image data and other previously proposed methods.