Name
Abstract | ||
|---|---|---|
Detecting anomaly of chest X-ray images by advanced technologies, such as deep learning, is an urgent need to improve the work efficiency and diagnosis accuracy. Fine-tuning existing deep learning networks for medical image processing suffers from over-fitting and low transfer efficiency. To overcome such limitations, we design a hierarchical convolutional neural network (CNN) structure for ChestX-ray14 and propose a new network CXNet-m1, which is much shorter, thinner but more powerful than fine-tuning. We also raise a novel loss function sin-loss, which can learn discriminative information from misclassified and indistinguishable images. Besides, we optimize the convolutional kernels of CXNet-m1 to achieve better classification accuracy. The experimental results show that our light model CXNet-m1 with sin-loss function achieves better accuracy rate, recall rate, Fl-score, and AUC value. It illustrates that designing a proper CNN is better than fine-tuning deep networks, and the increase of training data is vital to enhance the performance of CNN. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2018.2885997 | IEEE ACCESS |
Keywords | Field | DocType |
Chest X-Rays image,anomaly detection,deep neural network,self-adapting loss function | Anomaly detection,Computer vision,Computer science,Image based,Artificial intelligence,Deep learning,Distributed computing | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuaijing Xu | 1 | 7 | 2.40 |
| Hao Wu | 2 | 143 | 18.69 |
| Rongfang Bie | 3 | 547 | 68.23 |