MFEUsLNet: Skin cancer detection and classification using integrated AI with multilevel feature extraction-based unsupervised learning

Abstract

Skin Cancer is the most common form of the disease and is responsible for millions of deaths each year. Most of the relevant studies concentrate on algorithms that are based on machine learning, and few on deep learning as well. However, due to the several challenges in dermoscopic image acquisition, these algorithms are unable to deliver the highest possible level of accuracy and specificity. Therefore, this article implements skin cancer detection and classification (SCDC) system using multilevel feature extraction (MFE)-based artificial intelligence (AI) with unsupervised learning (USL), here after denoted as MFEUsLNet. Initially, the given skin images are preprocessed using bilateral filter, which removes the noise artifacts from the source images. Then, a well-known USL approach named K-means clustering (KMC) is used for segmentation of skin lesion, which can detect the affected skin lesion quite efficiently. Then, gray level co-occurrence matrix (GLCM), and redundant discrete wavelet transform (RDWT) are used for low level, texture and colour feature extraction. Finally, recurrent neural network (RNN) classifier is used to train with these multi-level features and classify the multiple types of skin cancer. The simulations proven that the proposed MFEUsLNet model is outperformed state-of-the-art SCDC approaches in terms of medical statistical quality metrics such as classification accuracy, specificity, precision, recall, F1-score, and sensitivity for ISIC-2020 dataset.

Keywords

• Skin cancer detection and classification
• K-means clustering
• Redundant discrete wavelet transform
• Gray level co-occurrence matrix
• Recurrent neural networks