Abstract:
Lung cancer is the number one cause of death in the Philippines and in the world. Multi-detector
CT scanners provide opportunity to examine thin-section CT images, which improves reader
detection of focal findings and characterization of nodules. However, the sensitivity of manual
detection of cancerous and non-cancerous lung nodules is reported to be 70-75% only. Lung
Nodule Detector and Classifier Tool is decision support software that aims to aid the health
professionals in detecting and classifying lung nodules. LNDCT uses algorithms which include
diffusion, binarization, wavelet edge detection, and morphological operations to automatically
segment the nodules. The tool uses LibSVM, an open source support vector machine software in
classifying malignant from benign nodules. Three testing sets were used to test the accuracy of
detection and classification using LNDCT. Set A contains lung images where most of the sizes
of the cancerous nodules are greater than 200 pixels. Set B contains a combination of different
sizes of cancerous nodules. Set C contains 400 training and 200 testing random data which came
from pre-extracted features computed using LNDCT. The accuracy of the system is reported to
be 93.87%, 81.33%, and 88.57% for sets A, B and C respectively. LNDCT is a tool which can
help health professionals in detection, classification, and feature computations of lung nodules.
