Efficient Machine Learning-based Approach for Brain Tumor Detection Using the CAD System

dc.contributor.authorGuerroudji, Mohamed Amine
dc.contributor.authorHadjadj, Zineb
dc.contributor.authorLichouri, Mohamed
dc.contributor.authorAmara, Kahina
dc.contributor.authorZenati, Nadia
dc.date.accessioned2024-02-27T13:43:15Z
dc.date.available2024-02-27T13:43:15Z
dc.date.issued2023-04
dc.description.abstractMedical research has focused on improving diagnosis through medical imaging in recent decades. Computer Assisted Diagnosis (CAD) systems have been developed to help doctors identify suspicious areas of interest, particularly those with cancer-like characteristics. CAD systems employ various algorithms and techniques to extract important numerical measurements from medical images that clinicians can use to evaluate patient conditions. This study proposes a statistical classification-based approach to efficient brain cancer detection. The proposed approach operates in three stages: first, Gradient Vector Flow (GVF) Snake models and mathematical morphology techniques retrieve regions of interest. The second stage characterizes these regions using morphological and textural parameters. Finally, a Bayesian network uses this description as input to identify malignant and benign cancer classes. We also compared the performance of the Bayesian network with other popular classification algorithms, including SVM, MLP, KNN, Random Forest, Decision Tree, XGBoost, LGBM, Gaussian Process, and RBF SVM. The results showed the superiority of the Bayesian network for the task of brain tumor classification. The proposed approach has been experimentally validated, with a sensitivity of 100% and a classification accuracy of over 98% for tumors, demonstrating the high efficiency of cancer cell segmentation.
dc.identifier.doihttps://doi.org/10.1080/03772063.2023.2196950
dc.identifier.issn0974-780X
dc.identifier.urihttps://dl.cerist.dz/handle/CERIST/1026
dc.publisherTaylor & Francis
dc.relation.ispartofseriesIETE Journal of Research
dc.relation.pages15p.
dc.structureAnalyse et modélisation de systèmes pour l'aide à la décision
dc.subjectBayesian network
dc.subjectBrain tumor
dc.subjectCAD
dc.subjectGVF
dc.subjectMachine learning
dc.subjectSegmentation
dc.subjectComputer Assisted Diagnosis
dc.subjectGradient Vector Flow
dc.titleEfficient Machine Learning-based Approach for Brain Tumor Detection Using the CAD System
dc.typeArticle
Files