An automated hybrid attention based deep convolutional capsule with weighted autoencoder approach for skin cancer classification

Abstract

ABSTRACT Skin cancer is a serious cancer caused by the uncontrollable growth of damaged DNA that leads to death. It is essential to identify the disease at the initial stage and eliminate it from spreading. Hence, this research introduces an automated hybrid deep learning (DL) technique for improving the accuracy of cancer diagnostic systems. In the pre-processing, histogram stretching, colour constancy, hair removal and noise elimination process are undertaken. Then, the Adaptive fuzzy c-means clustering (AFC) is introduced for segmenting the tumour portion. Then, the feature extraction and classification stage is performed using an attention-based deep convolutional capsule weighted auto-encoder classifier network (A-DCCN-WAE) technique. For experimentation, the dataset is collected from International Skin Imaging Collaboration (ISIC) 2019 dataset and is implemented in the PYTHON platform. The proposed method obtains an accuracy of 97%, Precision of 95.6%, F-measure of 97.2%, Mathew's correlation coefficient (MCC) of 90.6% and specificity of 96.9%.