X-RAY IMAGE CLASSIFICATION USING DOMAIN TRANSFERRED CONVOLUTIONAL NEURAL NETWORKS AND LOCAL SPARSE SPATIAL PYRAMID

The classification of medical images is a critical step for imaging-based clinical decision support systems. Existing classification methods for X-ray images, however, generally represent the image using only local texture or generic image features (e.g. color or shape) derived from pre-defined feature spaces. This limits the ability to quantify the image characteristics using general data-derived features learned from image datasets. In this study we present a new algorithm to improve the performance of X-ray image classification, where we propose a late-fusion of domain transferred convolutional neural networks (DT-CNNs) with sparse spatial pyramid (SSP) features derived from a local image dictionary. Our method is robust as it exploits the rich generic information provided by the DT-CNNs and uses the specific local features and characteristics inherent in the Xray images. Our method was evaluated on a public dataset of X-ray images and was compared to several state-of-theart approaches. Experimental results show that our method was the most accurate for classification.