A framework to build accurate Convolutional Neural Network models for melanoma diagnosis

In the past few years, Convolutional Neural Networks have achieved performance levels similar to those achieved by dermatologists. However, the diagnosis of melanoma remains a challenging task, mainly due to the high levels of inter and intra-class variability present in images of moles. With the aim of new methods for an effective melanoma diagnosis, a new framework is proposed. The training process is guided by an expert within an active learning approach where the architectures implicitly learn about the complexity of individual images through query strategies, which allows us to adjust the training process and achieve better performance. In addition, we propose a batch-based query strategy that enables a more stable and faster training process. Besides, the framework leverages segmentation, data augmentation and transfer learning to enhance melanoma diagnosis. The framework is composed by several specialized blocks, which allow us to measure how the diagnosis is improved after each step. In this sense, blocks could be customized and do not depend on specific models. An extensive experimental study was conducted on 16 skin image datasets, where five state-of-the-art models were significantly outperformed. This study corroborated that new active learning query strategies can be employed to effectively train neural networks architectures for the diagnosis of melanoma, achieving 182% better predictive performance in Xception, and an overall 11% and 20% better predictive performance in dermoscopic and non-dermoscopic images, respectively. It is worth mentioning that the informativeness value of each image is shown, which leads to identify the hardest images for the predictive models. Finally, the proposal required 2% of the total training time, and needed 61% less training epochs.