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

Hamdalla F. Al-Yasriy, Muayed S AL-HUSEINY, Furat Y. Mohsen, Enam A. Khalil, Zainab S. Hassan

2021Indonesian Journal of Electrical Engineering and Computer Science113 citationsDOI Open Access PDF

Abstract

<p>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%.</p>

Topics & Concepts

Support vector machinePreprocessorArtificial intelligencePattern recognition (psychology)Lung cancerComputer scienceFeature extractionSegmentationFeature (linguistics)Image processingImage (mathematics)Computer visionMedicinePathologyLinguisticsPhilosophyRadiomics and Machine Learning in Medical ImagingAI in cancer detectionBrain Tumor Detection and Classification