Name
Title | ||
|---|---|---|
A framework of predicting drug resistance of lung tuberculosis by utilizing radiological images |
Abstract | ||
|---|---|---|
Drug-resistant tuberculosis (TB) has been a persistent death thread of human health for hundreds of years. The increasing emergence of drug resistance and extensively drug-resistant Mycobacterium TB raise researchers attentions. And how to predict drug-resistant lung TB quickly and effectively has become a big challenge. This paper reviews the major prediction methods of drug-resistant lung tuberculosis appeared in recent years. Specifically, we survey the development of prediction methods of lung TB drug resistance, lung region segmentation, and features selection in different radiological images (CT and X-ray images). Furthermore, we summarize a framework which is suitable for the prediction process based on previous literatures. However, this process need human participation and the accuracy rate is not very high. Thus, to address this problem, we introduce deep learning algorithms into this field and present a proved framework to predict automatically, due to the superior performance of deep learning techniques in other medical image analysis fields, and get a high accuracy. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICACI.2018.8377474 | 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI) |
Keywords | Field | DocType |
lung tuberculosis classification,drug resistance,transfer learning | Segmentation,Drug resistance,Computer science,Feature extraction,Lung tuberculosis,Artificial intelligence,Deep learning,Tuberculosis,Machine learning,Radiological weapon,Human health | Conference |
ISBN | Citations | PageRank |
978-1-5386-4363-1 | 0 | 0.34 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianping Yin | 1 | 978 | 89.94 |
| Mengchi Lu | 2 | 0 | 0.34 |
| Long Gao | 3 | 1 | 2.04 |
| Xifeng Guo | 4 | 56 | 8.35 |