Cell classification using convolutional neural networks in medical hyperspectral imagery

Hyperspectral imaging is a rising imaging modality in the field of medical applications, and the combination of both spectral and spatial information provides wealth information for cell classification. In this paper, deep convolutional neural network (CNN) is employed to achieve blood cell discrimination in medical hyperspectral images (MHSI). As a deep learning architecture, CNNs are expected to get more discriminative and semantic features, which effect classification accuracy to a certain extent. Experimental results based on two real medical hyperspectral image data sets demonstrate that cell classification using CNNs is effective. In addition, compared to traditional support vector machine (SVM), the proposed method, which jointly exploits spatial and spectral features, can achieve better classification performance, showcasing the CNN-based methods' tremendous potential for accurate medical hyperspectral data classification.