End-to-End Deep Diagnosis of X-ray Images

• URL: http://arxiv.org/abs/2003.08605v1
• Date: Thu, 19 Mar 2020 07:20:48 GMT
• Title: End-to-End Deep Diagnosis of X-ray Images
• Authors: Kudaibergen Urinbayev, Yerassyl Orazbek, Yernur Nurambek, Almas Mirzakhmetov, and Huseyin Atakan Varol
• Abstract summary: We present an end-to-end deep learning framework for X-ray image diagnosis. As the first step, our system determines whether a submitted image is an X-ray or not. After it classifies the type of the X-ray, it runs a dedicated abnormality classification network.
• Score: 1.7779154193694813
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we present an end-to-end deep learning framework for X-ray image diagnosis. As the first step, our system determines whether a submitted image is an X-ray or not. After it classifies the type of the X-ray, it runs the dedicated abnormality classification network. In this work, we only focus on the chest X-rays for abnormality classification. However, the system can be extended to other X-ray types easily. Our deep learning classifiers are based on DenseNet-121 architecture. The test set accuracy obtained for 'X-ray or Not', 'X-ray Type Classification', and 'Chest Abnormality Classification' tasks are 0.987, 0.976, and 0.947, respectively, resulting into an end-to-end accuracy of 0.91. For achieving better results than the state-of-the-art in the 'Chest Abnormality Classification', we utilize the new RAdam optimizer. We also use Gradient-weighted Class Activation Mapping for visual explanation of the results. Our results show the feasibility of a generalized online projectional radiography diagnosis system.

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