Enhancing Classification of Active and Non-Active Lesions in Multiple Sclerosis: Machine Learning Models and Feature Selection Techniques Publisher Pubmed



Rostami A^{1, 2} ; Robatjazi M^{1, 2} ; Dareyni A³ ; Ghorbani AR⁴ ; Ganji O⁵ ; Siyami M⁶ ; Raoofi AR⁷ Show Affiliations
Source: BMC Medical Imaging Published:2024

Abstract

Introduction: Gadolinium-based T1-weighted MRI sequence is the gold standard for the detection of active multiple sclerosis (MS) lesions. The performance of machine learning (ML) and deep learning (DL) models in the classification of active and non-active MS lesions from the T2-weighted MRI images has been investigated in this study. Methods: 107 Features of 75 active and 100 non-active MS lesions were extracted by using SegmentEditor and Radiomics modules of 3D slicer software. Sixteen ML and one sequential DL models were created using the 5-fold cross-validation method and each model with its special optimized parameters trained using the training-validation datasets. Models’ performances in test data set were evaluated by metric parameters of accuracy, precision, sensitivity, specificity, AUC, and F1 score. Results: The sequential DL model achieved the highest AUC of 95.60% on the test dataset, demonstrating its superior ability to distinguish between active and non-active plaques. Among traditional ML models, the Hybrid Gradient Boosting Classifier (HGBC) demonstrated a commendable test AUC of 86.75%, while the Gradient Boosting Classifier (GBC) excelled in cross-validation with an AUC of 87.92%. Conclusion: The performance of sixteen ML and one sequential DL models in the classification of active and non-active MS lesions was evaluated. The results of the study highlight the effectiveness of sequential DL approach and ensemble methods in achieving robust predictive performance, underscoring their potential applications in classifying MS plaques. © The Author(s) 2024.