Automated lung tumor detection and diagnosis in CT Scans using texture feature analysis and SVM

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 successfully segments 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.