Lung Nodule Detection Using Combined Traditional and Deep Models and Chest CT
| Autor: | Yong Xia, Tairan Huang, Zhaowei Huang, Junjie Zhang, Yanning Zhang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Nodule detection
Lung Average diameter business.industry Computer science Chest ct Nodule (medicine) medicine.disease Object detection 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine medicine.anatomical_structure 030220 oncology & carcinogenesis medicine medicine.symptom Lung cancer Nuclear medicine business |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030026974 IScIDE |
| Popis: | Detection of lung nodules in chest CT scans is of great value to the early diagnosis of lung cancer. In this paper, we jointly use traditional object detection methods and deep learning, and thus propose a lung nodule detection algorithm for chest CT scans. We first detect all candidate nodules using multi-scale Laplace of Gaussian (LoG) filters and shape priors, and finally construct a multi-scale 3D DCNN to differentiate nodules from non-nodule volumes and estimate nodules’ diameters simultaneously. This algorithm has been evaluated on the benchmark LUng Nodule Analysis 2016 (LUNA16) dataset and achieved an average diameter estimation error of 0.98 mm and a detection score of 0.913. Our results suggest that the proposed algorithm can effectively detect lung nodules on chest CT scans and accurately estimate their diameters. |
| Databáze: | OpenAIRE |
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