Classification of clinical skin lesions with double-branch networks

Abstract

IntroductionMalignant skin lesions pose a great threat to the health of patients. Due to the limitations of existing diagnostic techniques, such as poor accuracy and invasive operations, malignant skin lesions are highly similar to other skin lesions, with low diagnostic efficiency and high misdiagnosis rates. Automatic medical image classification using computer algorithms can effectively improve clinical diagnostic efficiency. However, existing clinical datasets are sparse and clinical images have complex backgrounds, problems with noise interference such as light changes and shadows, hair occlusions, etc. In addition, existing classification models lack the ability to focus on lesion regions in complex backgrounds.MethodsIn this paper, we propose a DBN (double branch network) based on a two-branch network model that uses a backbone with the same structure as the original network branches and the fused network branches. The feature maps of each layer of the original network branch are extracted by our proposed CFEBlock (Common Feature Extraction Block), the common features of the feature maps between adjacent layers are extracted, and then these features are combined with the feature maps of the corresponding layers of the fusion network branch by FusionBlock, and finally the total prediction results are obtained by weighting the prediction results of both branches. In addition, we constructed a new dataset CSLI (Clinical Skin Lesion Images) by combining the publicly available dataset PAD-UFES-20 with our collected dataset, the CSLI dataset contains 3361 clinical dermatology images for six disease categories: actinic keratosis (730), cutaneous basal cell carcinoma (1136), malignant melanoma (170) cutaneous melanocytic nevus (391), squamous cell carcinoma (298) and seborrheic keratosis (636).ResultsWe divided the CSLI dataset into a training set, a validation set and a test set, and performed accuracy, precision, sensitivity, specificity, f1score, balanced accuracy, AUC summary, visualisation of different model training, ROC curves and confusion matrix for various diseases, ultimately showing that the network performed well overall on the test data.DiscussionThe DBN contains two identical feature extraction network branches, a structure that allows shallow feature maps for image classification to be used with deeper feature maps for information transfer between them in both directions, providing greater flexibility and accuracy and enhancing the network's ability to focus on lesion regions. In addition, the dual branch structure of DBN provides more possibilities for model structure modification and feature transfer, and has great potential for development.