  In this paper, we studied extensively on different deep learning based
methods to detect melanoma and skin lesion cancers. Melanoma, a form of
malignant skin cancer is very threatening to health. Proper diagnosis of
melanoma at an earlier stage is crucial for the success rate of complete cure.
Dermoscopic images with Benign and malignant forms of skin cancer can be
analyzed by computer vision system to streamline the process of skin cancer
detection. In this study, we experimented with various neural networks which
employ recent deep learning based models like PNASNet-5-Large,
InceptionResNetV2, SENet154, InceptionV4. Dermoscopic images are properly
processed and augmented before feeding them into the network. We tested our
methods on International Skin Imaging Collaboration (ISIC) 2018 challenge
dataset. Our system has achieved best validation score of 0.76 for
PNASNet-5-Large model. Further improvement and optimization of the proposed
methods with a bigger training dataset and carefully chosen hyper-parameter
could improve the performances. The code available for download at
https://github.com/miltonbd/ISIC_2018_classification
