1 research outputs found
Multiple Convolutional Neural Network for Skin Dermoscopic Image Classification
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.Comment: 9 pages, ISIC 2018: Skin Lesion Analysis Towards Melanoma Detectio