Evaluation of a Support Vector Machine Based Method for Crohn’s Disease Classification
- Authors: Franchini S.; Terranova M.C.; Lo Re G.; Salerno S.; Midiri M.; Vitabile S.
- Publication year: 2020
- Type: Capitolo o Saggio
- OA Link: http://hdl.handle.net/10447/435850
Crohn’s disease (CD) is a chronic, disabling inflammatory bowel disease that affects millions of people worldwide. CD diagnosis is a challenging issue that involves a combination of radiological, endoscopic, histological, and laboratory investigations. Medical imaging plays an important role in the clinical evaluation of CD. Enterography magnetic resonance imaging (E-MRI) has been proven to be a useful diagnostic tool for disease activity assessment. However, the manual classification process by expert radiologists is time-consuming and expensive. This paper proposes the evaluation of an automatic Support Vector Machine (SVM) based supervised learning method for CD classification. A real E-MRI dataset composed of 800 patients from the University of Palermo Policlinico Hospital (400 patients with histologically proved CD and 400 healthy patients) has been used to evaluate the proposed classification technique. For each patient, a team of radiology experts has extracted a vector composed of 20 features, usually associated with CD, from the related E-MRI examination, while the histological specimen results have been used as the ground-truth for CD diagnosis. The dataset composed of 800 vectors has been used to train and validate the SVM classifier. Automatic techniques for feature space reduction have been applied and validated by the radiologists to optimize the proposed classification method, while K-fold cross-validation has been used to improve the SVM classifier reliability. The measured indexes (sensitivity: 97.07%, specificity: 96.04%, negative predictive value: 97.24%, precision: 95.80%, accuracy: 96.54%, error: 3.46%) are better than the operator-based reference values reported in the literature. Experimental results also show that the proposed method outperforms the main standard classification techniques.