Back to Search
Start Over
Automatic Detection and Staging of Lung Tumors using Locational Features and Double-Staged Classifications
- Source :
- Applied Sciences, Vol 9, Iss 11, p 2329 (2019), Applied Sciences, Volume 9, Issue 11
- Publication Year :
- 2019
- Publisher :
- MDPI AG, 2019.
-
Abstract
- Lung cancer is a life-threatening disease with the highest morbidity and mortality rates of any cancer worldwide. Clinical staging of lung cancer can significantly reduce the mortality rate, because effective treatment options strongly depend on the specific stage of cancer. Unfortunately, manual staging remains a challenge due to the intensive effort required. This paper presents a computer-aided diagnosis (CAD) method for detecting and staging lung cancer from computed tomography (CT) images. This CAD works in three fundamental phases: segmentation, detection, and staging. In the first phase, lung anatomical structures from the input tomography scans are segmented using gray-level thresholding. In the second, the tumor nodules inside the lungs are detected using some extracted features from the segmented tumor candidates. In the last phase, the clinical stages of the detected tumors are defined by extracting locational features. For accurate and robust predictions, our CAD applies a double-staged classification: the first is for the detection of tumors and the second is for staging. In both classification stages, five alternative classifiers, namely the Decision Tree (DT), K-nearest neighbor (KNN), Support Vector Machine (SVM), Ensemble Tree (ET), and Back Propagation Neural Network (BPNN), are applied and compared to ensure high classification performance. The average accuracy levels of 92.8% for detection and 90.6% for staging are achieved using BPNN. Experimental findings reveal that the proposed CAD method provides preferable results compared to previous methods<br />thus, it is applicable as a clinical diagnostic tool for lung cancer.
- Subjects :
- Computer science
Decision tree
CAD
lcsh:Technology
030218 nuclear medicine & medical imaging
lcsh:Chemistry
03 medical and health sciences
0302 clinical medicine
backpropagation neural network
medicine
General Materials Science
Segmentation
Stage (cooking)
Lung cancer
lcsh:QH301-705.5
Instrumentation
Fluid Flow and Transfer Processes
lcsh:T
business.industry
Process Chemistry and Technology
General Engineering
Cancer
computed tomography
Pattern recognition
staging
medicine.disease
Thresholding
lcsh:QC1-999
automatic diagnosis
Computer Science Applications
Support vector machine
lung cancer
lcsh:Biology (General)
lcsh:QD1-999
lcsh:TA1-2040
030220 oncology & carcinogenesis
Artificial intelligence
lcsh:Engineering (General). Civil engineering (General)
business
lcsh:Physics
Subjects
Details
- ISSN :
- 20763417
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- Applied Sciences
- Accession number :
- edsair.doi.dedup.....a52addc1b56dee71f4cbab1435d14a97