Image classification toward lung cancer recognition by learning deep quality model

Image classification aims to automatically group a set of images into several categorizations, which is widely applied in scene categorization, image clustering. Lung cancer recognition can be achieved by using image classification technique, since there are distinct differences between healthy lung and sick lung images. In this paper, we propose lung cancer recognition based on image quality assessment, which can distinguish sick lung images from healthy lung images. First, our dataset is acquired using low-dose CT scan combined with full-mode iterative recombination (IMR). Then, we incorporate both low-level and high-level features to extract deep representation from obtained dataset. Specifically, our designed low-level features include color moment and texture feature, and CNN based method is leveraged for deep feature extraction. For reducing artifacts and noise of images, we assign quality score for each training image. And quality score and deep feature are fused to generate deep representation. Afterward, we propose a probabilistic model to learn the distribution of deep representation. Finally, lung cancer recognition can be achieved using learned model. We conduct comprehensive experiments and our proposed method is verified effective. (C) 2019 Published by Elsevier Inc.