Skin Lesion Classification Using Deep Neural Network

This paper reports the methods and techniques we have developed for classify dermoscopic images (task 1) of the ISIC 2019 challenge dataset for skin lesion classification, our approach aims to use ensemble deep neural network with some powerful techniques to deal with unbalance data sets as its the main problem for this challenge in a move to increase the performance of CNNs model.

[1]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Jorge S. Marques,et al.  Improving Dermoscopy Image Classification Using Color Constancy , 2015, IEEE Journal of Biomedical and Health Informatics.

[3]  Kaiming He,et al.  Focal Loss for Dense Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[4]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Lorenzo Porzi,et al.  In-place Activated BatchNorm for Memory-Optimized Training of DNNs , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.