An Inception Module CNN Classifiers Fusion Method on Pulmonary Nodule Diagnosis by Signs
文献类型：期刊论文
| 作者 | Zheng Guangyuan2; Han Guanghui1; Soomro Nouman Qadeer4 |
| 刊名 | TSINGHUA SCIENCE AND TECHNOLOGY
 |
| 出版日期 | 2020 |
| 卷号 | 25期号:3页码:368 |
| 关键词 | COMPUTER-AIDED DIAGNOSIS HIGH-RESOLUTION CT LUNG-CANCER NEURAL-NETWORKS IMAGING SIGNS CLASSIFICATION SURVIVAL SYSTEM PERFORMANCE PATTERNS sign lung cancer pulmonary nodule Convolutional Neural Network (CNN) Artificial Immune Algorithm (AIA) |
| ISSN号 | 1007-0214 |
| 英文摘要 | A "sign" on a lung CT image refers to a radiologic finding that suggests a pathological progression of some specific disease. Analysis of CT signs is helpful to understand the pathological origin of the lesion. In-depth study of lung nodules classification with different CT signs will help to distinguish benign and malignant nodules more clearly and accurately. To this end, we propose an Inception module-based ensemble classification method for pulmonary nodule diagnosis with different nodule signs. We first construct a Convolutional Neural Network (CNN) classifier adopting Inception modules and pre-train it on ImageNet. We then fine-tune this pre-trained classifier on 10 different lung nodule sign sample sets, and fuse these 10 classifiers with an artificial immune ensemble algorithm. The overall sensitivity, specificity, and accuracy of our proposed Artificial Immune Algorithm-based Inception Networks Fusion (AIA-INF) algorithm are 82.22%, 93.17%, and 88.67%, respectively, which are significantly higher than those of the alternative Bagging and Boosting methods. The experimental results show that our Inception-based ensemble classifier offers promising performance, and compared with other CADx systems, this scheme can offer a more detailed reference for diagnosis, and can be valuable for junior radiologist training. |
| 语种 | 英语 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/27470]  |
| 专题 | 中国科学院计算技术研究所期刊论文_中文 |
| 作者单位 | 1.中国科学院广州地球化学研究所 2.中国科学院计算技术研究所 3.商丘师范学院 4.Mehran University Engn & Technol, Dept Software Engn, SZAB Campus, Khairpur Mirs 66020, Pakistan |
| 推荐引用方式 GB/T 7714 | Zheng Guangyuan,Han Guanghui,Soomro Nouman Qadeer. An Inception Module CNN Classifiers Fusion Method on Pulmonary Nodule Diagnosis by Signs[J]. TSINGHUA SCIENCE AND TECHNOLOGY,2020,25(3):368. |
| APA | Zheng Guangyuan,Han Guanghui,&Soomro Nouman Qadeer.(2020).An Inception Module CNN Classifiers Fusion Method on Pulmonary Nodule Diagnosis by Signs.TSINGHUA SCIENCE AND TECHNOLOGY,25(3),368. |
| MLA | Zheng Guangyuan,et al."An Inception Module CNN Classifiers Fusion Method on Pulmonary Nodule Diagnosis by Signs".TSINGHUA SCIENCE AND TECHNOLOGY 25.3(2020):368. |
入库方式： OAI收割
来源：计算技术研究所
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