Skin cancer diagnosis using convolutional neural networks for smart phone images: A comparative study

In the past few years many researches investigated algorithms to diagnose skin cancer lesions where melanoma is the deadliest type of skin cancer. In this paper, we propose three different methods for binary classification (melanoma and nevus) using smart phone images since there is a lack in research on it. The applied methods depend on different versions of CNN architectures namely; Alex-net, Mobilenet-V2, and Resnet-50. These three methods are divided into two groups; one group is the three nets with transfer learning applied on the original data, and the second group is the same methods used for group one but applied on augmented data. The proposed methods are applied on PAD-UFES-20 which is a smart phone images dataset. The augmented data have improved the performance of each method relative to its performance for original data. The best performance is achieved when using Alex-net with transfer learning (TL) and data augmentation, as it achieved 99% for accuracy, 96.364% for sensitivity, 99.792% for specificity, 99.167% for precision, and 97.661% for F1-score. Mobilenet-V2 with TL on augmented dataset achieved promising results if it used as a mobile application as it achieved 94.071% for accuracy, 84.547% for sensitivity, 96.25% for specificity, 84.223% for precision, and 84.2% for F1-score. And finally, Resnet-50 with TL on augmented dataset achieved 94.918% for accuracy, 77.275% for sensitivity, 98.918% for specificity, 95.192% for precision, and 84.737% for F1-score