Medical Hyperspectral Image Classification Based on End-To-End Fusion Deep Neural Network

To solve the problem of supervised convolutional neural network (CNN) models suffering from limited samples, a two-channel CNN is developed for medical hyperspectral images (MHSI) classification tasks. In the proposed network, one channel of end-To-end network, denoted as EtoE-Net, is designed to realize unsupervised learning, obtaining representative and global fused features with fewer noises, by building pixel-by-pixel mapping between the two source data, i.e., the original MHSI data and its principal component. On the other hand, a simple but efficient CNN is employed to supply local detailed information. The features extracted from different underlying layers of two channels (i.e., EtoE-Net and typical CNN) are concatenated into a vector, which is expected to preserve global and local informations simultaneously. Furthermore, the two-channel deep fusion network, named as EtoE-Fusion, is designed, where the full connection is employed for feature dimensionality reduction. To evaluate the effectiveness of the proposed framework, experiments on two MHSI data sets are implemented, and results confirm the potentiality of the proposed method in MHSI classification.