Name
Title | ||
|---|---|---|
DeepCXray: Automatically Diagnosing Diseases on Chest X-Rays Using Deep Neural Networks. |
Abstract | ||
|---|---|---|
The automatic detection of diseases in images acquired through chest X-rays can be useful in clinical diagnosis because of a shortage of experienced doctors. Compared with natural images, those acquired through chest X-rays are obtained by using penetrating imaging technology, such that there are multiple levels of features in an image. It is thus difficult to extract the features of a disease for further diagnosis. In practice, healthy people are in a majority and the morbidities of different disease vary, because of which the obtained labels are imbalanced. The two main challenges of diagnosis though chest X-ray images are to extract discriminative features from X-ray images and handle the problem of imbalanced data distribution. In this paper, we propose a deep neural network called DeepCXray that simultaneously solves these two problems. An Inception V3 model is trained to extract features from raw images, and a new objective function is designed to address the problem of imbalanced data distribution. The proposed objective function is a performance index based on cross entropy loss that automatically weights the ratio of positive to negative samples. In other words, the proposed loss function can automatically reduce the influence of an overwhelming number of negative samples by shrinking each cross entropy terms by a different extent. Extensive experiments highlight the promising performance of DeepCXray on the ChestXray14 dataset of the National Institutes of Health in terms of the area under the receiver operating characteristic curve. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2018.2875406 | IEEE ACCESS |
Keywords | Field | DocType |
Chest X-rays,deep neural networks,cross weighted cross entropy loss,imbalanced data,feature extraction | Cross entropy,Imaging technology,Receiver operating characteristic,Pattern recognition,Computer science,Feature extraction,Artificial intelligence,Artificial neural network,Discriminative model,Economic shortage,Deep neural networks,Distributed computing | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiuyuan Xu | 1 | 2 | 1.39 |
| Guo Quan | 2 | 64 | 7.27 |
| Jixiang Guo | 3 | 24 | 3.66 |
| Zhang Yi | 4 | 1765 | 194.41 |