Automated lung tumor detection and diagnosis in CT Scans using texture feature analysis and SVM
Journal Title: Annals of Computer Science and Information Systems - Year 2018, Vol 17, Issue
Abstract
CT scans are an important tool in the diagnosis of lung tumors in medicine. This work presents an automated system for lung tumor diagnosis on CT scans. Scans are automatically segmented using marker-based watershed transformation, which enables to successfully segment hardly separable, lung wall adjunct tumors. The scans are further analyzed in a sliding window approach using Haralick features and a Support Vector Machine classifier to detect and classify benign and malignant tumors. This novel approach for classification was tested using the LUNGx Challenge dataset and achieved exceptional results while utilizing a minimal training set.
Authors and Affiliations
Tim Adams, Jens Dörpinghaus, Marc Jacobs, Volker Steinhage
Keywords
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- EP ID EP568230
- DOI 10.15439/2018F176
- Views 39
- Downloads 0
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