Fuzzy-Label Weighted Deep Learning Classification for CT Image Quality Evaluation

This paper proposes a fuzzy-label weighted deep learning-based image classification approach for assessing computed tomography (CT) image quality. More specifically, we want to determine if a captured CT image passes Quality Assessment (QA) with a certain radiation dose. Our contributions here include proposing a fuzzy-label weighting method and introduces the concept of a "fuzzy-label" (to reflect the confidence of the ground-truth annotation by annotator) to aid in the training of deep learning-based image classification. We proposed an ensemble/assimilation method to determine the image quality at entire CT image-level using CT windowing (i.e. clipping of the CT image to 8-bit grayscale with respect to the various window-width (WW) and window-length (WL)), similar to what a human would do manually to assess the CT image quality in the factory setting. Experimental results showed that our proposed fuzzy-label weighted deep learning-based image classification approach (trained using annotations provided by one single annotator) significantly outperforms that of its traditional baseline image classification approach.