AHCI RESEARCH GROUP

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OUR RESEARCH

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2021

Franchini, Silvia; Terranova, Maria Chiara; Re, Giuseppe Lo; Galia, Massimo; Salerno, Sergio; Midiri, Massimo; Vitabile, Salvatore

A Novel System for Multi-level Crohn's Disease Classification and Grading Based on a Multiclass Support Vector Machine Book Section

In: Esposito, Anna; Faundez-Zanuy, Marcos; Morabito, Francesco Carlo; Pasero, Eros (Ed.): Progresses in Artificial Intelligence and Neural Systems, pp. 185–197, Springer, Singapore, 2021, ISBN: 9789811550935.

Abstract | Links | BibTeX | Tags: Bayesian optimization, Crohn's disease multi-level classification and grading, Feature extraction, Feature reduction, K-fold cross-validation, machine learning, Magnetic Resonance Enterography, Medical Imaging, multi-level classifiers, Multiclass support vector machines, Supervised learning

@incollection{franchiniNovelSystemMultilevel2021,

title = {A Novel System for Multi-level Crohn's Disease Classification and Grading Based on a Multiclass Support Vector Machine},

author = { Silvia Franchini and Maria Chiara Terranova and Giuseppe Lo Re and Massimo Galia and Sergio Salerno and Massimo Midiri and Salvatore Vitabile},

editor = { Anna Esposito and Marcos {Faundez-Zanuy} and Francesco Carlo Morabito and Eros Pasero},

doi = {10.1007/978-981-15-5093-5_18},

isbn = {9789811550935},

year  = {2021},

date = {2021-01-01},

urldate = {2023-03-20},

booktitle = {Progresses in Artificial Intelligence and Neural Systems},

pages = {185--197},

publisher = {Springer},

address = {Singapore},

series = {Smart Innovation, Systems and Technologies},

abstract = {Crohn's disease (CD) is a chronic inflammatory condition of the gastrointestinal tract that can highly alter patient's quality of life. Diagnostic imaging, such as Enterography Magnetic Resonance Imaging (E-MRI), provides crucial information for CD activity assessment. Automatic learning methods play a fundamental role in the classification of CD and allow to avoid the long and expensive manual classification process by radiologists. This paper presents a novel classification method that uses a multiclass Support Vector Machine (SVM) based on a Radial Basis Function (RBF) kernel for the grading of CD inflammatory activity. To validate the system, we have used a dataset composed of 800 E-MRI examinations of 800 patients from the University of Palermo Policlinico Hospital. For each E-MRI image, a team of radiologists has extracted 20 features associated with CD, calculated a disease activity index and classified patients into three classes (no activity, mild activity and severe activity). The 20 features have been used as the input variables to the SVM classifier, while the activity index has been adopted as the response variable. Different feature reduction techniques have been applied to improve the classifier performance, while a Bayesian optimization technique has been used to find the optimal hyperparameters of the RBF kernel. K-fold cross-validation has been used to enhance the evaluation reliability. The proposed SVM classifier achieved a better performance when compared with other standard classification methods. Experimental results show an accuracy index of 91.45% with an error of 8.55% that outperform the operator-based reference values reported in literature.},

keywords = {Bayesian optimization, Crohn's disease multi-level classification and grading, Feature extraction, Feature reduction, K-fold cross-validation, machine learning, Magnetic Resonance Enterography, Medical Imaging, multi-level classifiers, Multiclass support vector machines, Supervised learning},

pubstate = {published},

tppubtype = {incollection}

}