Context Learning for Bone Shadow Exclusion in CheXNet Accuracy Improvement

• URL: http://arxiv.org/abs/2005.06189v1
• Date: Wed, 13 May 2020 07:29:03 GMT
• Title: Context Learning for Bone Shadow Exclusion in CheXNet Accuracy Improvement
• Authors: Minh-Chuong Huynh, Trung-Hieu Nguyen, Minh-Triet Tran
• Abstract summary: After ChestX-ray14 dataset containing over 100,000 frontal-view X-ray images of 14 diseases was released, several models were proposed with high accuracy. We develop a work flow for lung disease diagnosis in chest X-ray images, which can improve the average AUROC of the state-of-the-art model from 0.8414 to 0.8445.
• Score: 14.18754838863113
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Chest X-ray examination plays an important role in lung disease detection. The more accuracy of this task, the more experienced radiologists are required. After ChestX-ray14 dataset containing over 100,000 frontal-view X-ray images of 14 diseases was released, several models were proposed with high accuracy. In this paper, we develop a work flow for lung disease diagnosis in chest X-ray images, which can improve the average AUROC of the state-of-the-art model from 0.8414 to 0.8445. We apply image preprocessing steps before feeding to the 14 diseases detection model. Our project includes three models: the first one is DenseNet-121 to predict whether a processed image has a better result, a convolutional auto-encoder model for bone shadow exclusion is the second one, and the last is the original CheXNet.

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