Deep Attention Network for Melanoma Detection Improved by Color Constancy

Dermoscopy images classification is the major method for melanoma detection. Currently, the most effective method of image classification is deep learning. However, training a deep neural network requires huge amounts of training data, and it is difficult to collect enough dermoscopy images. In this paper, we introduce the deep attention network which is optimized by attention mechanism and fisher criterion. Experiment results showed that this hybrid neural network improved the recognition rate when sample size was insufficient. Besides, considering the color of dermoscopy image is an important factor for melanoma detection, and the illumination may alter the color of dermoscopy image. We researched the color constancy algorithms for color calibration and tested the impact of color constancy on our model. The result proved that color constancy can further improve the recognition rate.