The goal of this thesis is to create mobile applications that can leverage the power of deep learning to detect malignant melanoma in the early phase and save lives. Thus, it is imperative to extend the reach of such essential diagnostic care worldwide. In this thesis, we will first present three deep learning methodologies that entail constructions of convolutional neural networks in conjunction with the uses of modern transfer learning and regularization techniques. The proposed deep learning methodologies leverage a dynamic dataset to optimize performance of a skin cancer classification mobile application called ChekSkin. Dynamic datasets refer to the expansion of datasets from influx of new data. Furthermore, the proposed deep learning methodologies generate mobile compatible models by rendering and training 80,192 high quality images. We performed rigorous experiments to attain the following top-1 accuracies: 81% (overall accuracy on the test dataset) using advanced transfer learning and data augmentation techniques via TensorFlow, 85.7% by training a batch-normalized CNN from scratch, and 88.35% with the uses of potent feature extraction and data augmentation methods via Keras. Additionally, the ChekSkin app is tested in real-world situations in which there are drastic variations in lighting conditions and image quality. We have considered tests in both experimental and real-world settings as important metrics for life-saving mobile applications.