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jhbmi 2024, 11(1): 1-13 |
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Hedyehzadeh M, Yousefi M. Application of Machine Learning Methods to Predict the Survival Rate of Glioblastoma Patients Using MR Images. jhbmi 2024; 11 (1) :1-13
URL: http://jhbmi.ir/article-1-828-en.html
URL: http://jhbmi.ir/article-1-828-en.html
Assistant Professor, PhD. in Biomedical Engineering, Department of Biomedical Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran
Abstract: (1130 Views)
Introduction: In this study, a method for automatic prediction of the survival rate of patients with glioblastoma tumor based on machine learning methods and MRI images is presented.
Method: The data set used in this study is the BraTS 2017 database with 163 samples. Each sample of database images has four different imaging modalities as well as information such as the patient's overall life expectancy according to the patient's day and age. Database images are labeled into three categories, short-term, medium-term, and long-term, based on patient longevity after treatment. To improve the prediction results, different types of features were extracted and taught by different machine learning methods. The considered features include texture, volumetric, statistical, and deep features. The machine learning methods used include support vector machine, nearest neighbors, linear discriminant analysis, and decision tree.
Results: The best prediction accuracy based on the classification was obtained using deep features extracted by a pre-trained convolutional neural network (CNN) and by linear discriminant analysis.
Conclusion: Deep learning approaches showed a good performance in the prediction of medical parameters such as survival rate time.
Method: The data set used in this study is the BraTS 2017 database with 163 samples. Each sample of database images has four different imaging modalities as well as information such as the patient's overall life expectancy according to the patient's day and age. Database images are labeled into three categories, short-term, medium-term, and long-term, based on patient longevity after treatment. To improve the prediction results, different types of features were extracted and taught by different machine learning methods. The considered features include texture, volumetric, statistical, and deep features. The machine learning methods used include support vector machine, nearest neighbors, linear discriminant analysis, and decision tree.
Results: The best prediction accuracy based on the classification was obtained using deep features extracted by a pre-trained convolutional neural network (CNN) and by linear discriminant analysis.
Conclusion: Deep learning approaches showed a good performance in the prediction of medical parameters such as survival rate time.
Type of Study: Original Article |
Subject:
Artificial Intelligence in Healthcare
Received: 2023/12/1 | Accepted: 2024/04/9
Received: 2023/12/1 | Accepted: 2024/04/9
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