An uncontrolled growth of brain cells is known as a brain tumor. When brain tumors are accurately and promptly diagnosed using magnetic resonance imaging scans, it is easier to start the right treatment, track the tumor’s development over time, and select the best surgical techniques. This paper applies advanced and popular methods for preprocessing, segmentation, grading of tumors and lifetime prediction. On exploring various encoder-decoder architectures, UNet++ architecture was chosen for detecting brain tumor and obtained an accuracy of 98% and intersection over union score of 0.7483 during the testing phase. The segmented image is used to extract the radiomics features. Despite of several difficulties, data imbalance among the dataset is the common obstacle for survival prediction. To solve and increase the sample size and preserve the class distribution, the synthetic minority oversampling technique and adaptive synthetic approaches are used. After balancing the dataset, the important characteristics are selected using principal component analysis and tree-based feature selection techniques. The collected characteristics are used as input for machine learning techniques including stochastic gradient descent, decision tree, random forest, and support vector machine. The distinction between low-grade glioma and high-grade glioma is investigated as a binary classification. Accuracy, precision, recall, and F1score are used in the performance evaluation. The highest accuracy of 96% is achieved using stochastic gradient descent. Lifetime prediction of high-grade glioma patients is made using regression techniques: Linear, ridge, stochastic gradient descent, and extreme gradient boosting. We have obtained the least mean square error of 93726.45 using the extreme gradient boosting method. The proposed approach is contrasted with the most recent segmentation, grading, and lifetime prediction methods described in the literature.

Machine Learning Empowered Brain Tumor Segmentation and Grading Model for Lifetime Prediction

Pau, Giovanni
;
2023-01-01

Abstract

An uncontrolled growth of brain cells is known as a brain tumor. When brain tumors are accurately and promptly diagnosed using magnetic resonance imaging scans, it is easier to start the right treatment, track the tumor’s development over time, and select the best surgical techniques. This paper applies advanced and popular methods for preprocessing, segmentation, grading of tumors and lifetime prediction. On exploring various encoder-decoder architectures, UNet++ architecture was chosen for detecting brain tumor and obtained an accuracy of 98% and intersection over union score of 0.7483 during the testing phase. The segmented image is used to extract the radiomics features. Despite of several difficulties, data imbalance among the dataset is the common obstacle for survival prediction. To solve and increase the sample size and preserve the class distribution, the synthetic minority oversampling technique and adaptive synthetic approaches are used. After balancing the dataset, the important characteristics are selected using principal component analysis and tree-based feature selection techniques. The collected characteristics are used as input for machine learning techniques including stochastic gradient descent, decision tree, random forest, and support vector machine. The distinction between low-grade glioma and high-grade glioma is investigated as a binary classification. Accuracy, precision, recall, and F1score are used in the performance evaluation. The highest accuracy of 96% is achieved using stochastic gradient descent. Lifetime prediction of high-grade glioma patients is made using regression techniques: Linear, ridge, stochastic gradient descent, and extreme gradient boosting. We have obtained the least mean square error of 93726.45 using the extreme gradient boosting method. The proposed approach is contrasted with the most recent segmentation, grading, and lifetime prediction methods described in the literature.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11387/162985
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