A Novel Classification Method using Effective Neural Network and Quantitative Magnetization Transfer Imaging of Brain White Matter in Relapsing Remitting Multiple Sclerosis
محل انتشار: مجله فیزیک و مهندسی پزشکی، دوره: 8، شماره: 4
سال انتشار: 1397
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 63
فایل این مقاله در 14 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_JBPE-8-4_009
تاریخ نمایه سازی: 30 دی 1402
چکیده مقاله:
Background: Quantitative Magnetization Transfer Imaging (QMTI) is often used to quantify the myelin content in multiple sclerosis (MS) lesions and normal appearing brain tissues. Also, automated classifiers such as artificial neural networks (ANNs) can significantly improve the identification and classification processes of MS clinical datasets. Objective: We classified patients with relapsing-remitting multiple sclerosis (RRMS) from healthy subjects using QMTI and T۱ longitudinal relaxation time data of brain white matter, then the performance of three ANN-based classifiers have been investigated. Materials and Methods: The input features of ANN algorithms, including multilayer perceptron (MLP), radial basis function (RBF) and ensemble neural networks based on Akaike information criterion (ENN-AIC) were extracted in the form of QMTI and T۱ mean values from parametric maps. The ANNs quantitative performance is measured by the standard evaluation of confusion matrix criteria. Results: The results indicate that ENN-AIC-based classification method has achieved ۹۰% accuracy, ۹۲% sensitivity and ۸۶% precision compared to other ANN models. NPV, FPR and FDR values were found to be ۰.۹۳۳, ۰.۱۲۵ and ۰.۱۳۳, respectively, according to the proposed ENN-AIC model. A graphical representation of how to track actual data by the predictive values derived from ANN algorithms, was also presented.Conclusion: It has been demonstrated that ENN-AIC as an effective neural network improves the quality of classification results compared to MLP and RBF.In addition, this research provides a new direction to classify a large amount of quantitative MRI data that can help the physician in a correct MS diagnosis.
کلیدواژه ها:
Quantitative Magnetization Transfer Imaging ، Relapsing Remitting Multiple Sclerosis ، Artificial neural networks ، Magnetic Resonance Imaging
نویسندگان
M Fooladi
Medical Physics and Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
H Sharini
Medical Physics and Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
S Masjoodi
Medical Physics and Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
E Khodamoradi
Radiology and Nuclear Medicine Department, School of Allied Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran
مراجع و منابع این مقاله:
لیست زیر مراجع و منابع استفاده شده در این مقاله را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود مقاله لینک شده اند :