Evaluation of SVM performance in the detection of lung cancer in marked CT scan dataset

Hamdalla Fadil Kareem (wasit university)
Muayed S AL-Huseiny (Wasit University)
Furat Y. Mohsen (The Medical City)
Enam A. Khalil (The Medical City)
Zainab S. Hassan (The Medical City)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 21, No 3: March 2021

This paper concerns the development/analysis of the IQ-OTH/NCCD lung cancer dataset. This CT-scan dataset includes more than 1100 images of diagnosed healthy and tumorous chest scans collected in two Iraqi hospitals. A computer system is proposed for detecting lung cancer in the dataset by using image-processing/computer-vision techniques. This includes three preprocessing stages: image enhancement, image segmentation, and feature extraction techniques. Then, support vector machine (SVM) is used at the final stage as a classification technique for identifying the cases on the slides as one of three classes: normal, benign, or malignant. Different SVM kernels and feature extraction techniques are evaluated. The best accuracy achieved by applying this procedure on the new dataset was 89.8876%.