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

Hamdalla Fadil Kareem, Muayed S AL-Huseiny, Furat Y. Mohsen, Enam A. Khalil, Zainab S. Hassan


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%.


SVM Lung Cancer, CT-Scan, Computer Vision, Datasets.

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DOI: http://doi.org/10.11591/ijeecs.v21.i3.pp1731-1738


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