Skin-Net: a novel deep residual network for skin lesions classification using multilevel feature extraction and cross-channel correlation with detection of outlier

Abstract

Abstract Human Skin cancer is commonly detected visually through clinical screening followed by a dermoscopic examination. However, automated skin lesion classification remains challenging due to the visual similarities between benign and melanoma lesions. In this work, the authors proposed a new Artificial Intelligence-Based method to classify skin lesions. In this method, we used Residual Deep Convolution Neural Network. We implemented several convolution filters for multi-layer feature extraction and cross-channel correlation by sliding dot product filters instead of sliding filters along the horizontal axis. The proposed method overcomes the imbalanced dataset problem by converting the dataset from image and label to vector of image and weight. The proposed method is tested and evaluated using the challenging datasets ISIC-2019 & ISIC-2020. It outperformed the existing deep convolutional networks in the multiclass classification of skin lesions. Graphical Abstract

Keywords

• Skin lesions
• Residual learning
• Deep convolution neural network
• Classification
• Multilevel feature
• Cross-channel correlation