Dermoscopy lesion classification based on GANs and a fuzzy rank-based ensemble of CNN models. (21st September 2022)

• Record Type: Journal Article
• Authors: Li, Haiyan; Li, Wenqing; Chang, Jun; Zhou, Liping; Luo, Jin; Guo, Yifan
• Is Part Of: Physics in medicine & biology. Volume 67:Number 18(2022)
• Publication Date: 2022-09-21
• Subjects: skin lesion classification -- generative adversarial networks -- fuzzy rank-based ensemble -- multilayer feature fusion -- efficient channel integrating spatial attention -- united loss function; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/ac8b60
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Background and Objective . Skin lesion classification by using deep learning technologies is still a considerable challenge due to high similarity among classes and large intraclass differences, serious class imbalance in data, and poor classification accuracy with low robustness. Approach . To address these issues, a two-stage framework for dermoscopy lesion classification using adversarial training and a fuzzy rank-based ensemble of multilayer feature fusion convolutional neural network (CNN) models is proposed. In the first stage, dermoscopy dataset augmentation based on generative adversarial networks is proposed to obtain realistic dermoscopy lesion images, enabling significant improvement for balancing the number of lesions in each class. In the second stage, a fuzzy rank-based ensemble of multilayer feature fusion CNN models is proposed to classify skin lesions. In addition, an efficient channel integrating spatial attention module, in which a novel dilated pyramid pooling structure is designed to extract multiscale features from an enlarged receptive field and filter meaningful information of the initial features. Combining the cross-entropy loss function with the focal loss function, a novel united loss function is designed to reduce the intraclass sample distance and to focus on difficult and error-prone samples to improve the recognition accuracy of the proposed model. Main results . In this paper, the common dataset (HAM10000) is selected to conduct simulation experiments to evaluate and verify the effectiveness of the proposed method. The subjective and objective experimental results demonstrate that the proposed method is superior over the state-of-the-art methods for skin lesion classification due to its higher accuracy, specificity and robustness. Significance . The proposed method effectively improves the classification performance of the model for skin diseases, which will help doctors make accurate and efficient diagnoses, reduce the incidence rate and improve the survival rates of patients.