Multiple Convolutional Neural Network for Skin Dermoscopic Image Classification
This work addresses skin disease diagnosis for medical applications, but it appears incremental as it builds on existing CNN methods with a multi-model approach.
The authors tackled melanoma classification from dermoscopic images by proposing a multiple convolutional neural network (MCNN) model, achieving performance evaluated using AUC values on the ISIC 2018 dataset.
Melanoma classification is a serious stage to identify the skin disease. It is considered a challenging process due to the intra-class discrepancy of melanomas, skin lesions low contrast, and the artifacts in the dermoscopy images, including noise, existence of hair, air bubbles, and the similarity between melanoma and non-melanoma cases. To solve these problems, we propose a novel multiple convolution neural network model (MCNN) to classify different seven disease types in dermoscopic images, where several models were trained separately using an additive sample learning strategy. The MCNN model is trained and tested using the training and validation sets from the International Skin Imaging Collaboration (ISIC 2018), respectively. The receiver operating characteristic (ROC) curve is used to evaluate the performance of the proposed method. The values of AUC (the area under the ROC curve) were used to evaluate the performance of the MCNN.