Multiple Convolutional Neural Network for Skin Dermoscopic Image Classification

TL;DR

This paper introduces a novel multiple convolutional neural network (MCNN) model for classifying seven types of skin diseases in dermoscopic images, addressing challenges like low contrast and artifacts to improve melanoma detection.

Contribution

The paper presents a new MCNN architecture trained with an additive sample learning strategy for improved skin disease classification.

Findings

Achieved high AUC scores on ISIC 2018 dataset

Demonstrated effectiveness in handling artifacts and low contrast

Outperformed existing models in classification accuracy

Abstract

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.