Name
Abstract | ||
|---|---|---|
As the diagnosis of lung cancer, lung mass for the diagnosis of the disease is meaningful, chest radiography has low price, low radiation, popularity and other characteristics, it is a significant attempt for the location of chest masses on chest radiography using deep learning method. In this paper we have established a labeled lung mass database, and presented a state of the art deep learning methodology for classifying, detecting and locating lung masses on the database. Moreover we analyzed the details of the Faster RCNN network and its architecture, and studied the feature extraction parts by two different networks, both of them are deep learning method. To a certain extent, the two networks can locate the masses. We find that the methodology using RESNET for feature extraction is more satisfying than VGG16, the Ap achieved 52.38% by comparing the test results. The system retrieved 41 out of 51 masses in the testing phase. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/BIBM.2017.8217787 | 2017 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) |
Keywords | DocType | ISSN |
deep learning, lung masses, Faster RCNN, RESNET, chest radiography database | Conference | 2156-1125 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||