Ahn, Euijoon, Kumar, Ashnil, Kim, Jinman, Li, Changyang, Feng, Dagan, and Fulham, Michael (2016) X-ray image classification using domain transferred convolutional neural networks and local sparse spatial pyramid. In: Proceedings of the 13th International Symposium on Biomedical Imaging. pp. 855-858. From: ISBI 2016: 13th International Symposium on Biomedical Imaging, 13-16 April 20216, Prague, Czech Republic.

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Abstract

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 predefined 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 X-ray images. Our method was evaluated on a public dataset of X-ray images and was compared to several state-of-the-art approaches. Experimental results show that our method was the most accurate for classification.

Item ID:	72041
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-4799-2349-6
Copyright Information:	©2016 IEEE
Date Deposited:	12 May 2022 02:53
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460299 Artificial intelligence not elsewhere classified @ 40% 46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460306 Image processing @ 60%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220404 Computer systems @ 100%
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