Detection and Classification of Brain Tumours from MRI Images with Prediction of the Overall Survival Rate in Glioblastoma Using Machine Learning Techniques
Keywords:
Survival prediction, Glioblastoma multiforme, Brain tumour segmentation, U-Net , Machine learningAbstract
Classifying a brain tumour is a crucial first step in establishing whether or not the abnormal tissues present a lethal threat to the patient and creating an appropriate treatment strategy for the latter's recovery. The most dangerous and rapidly-growing variety of glial tumour, glioblastoma multiforme (GBM) is most commonly referred to by its acronyms, glioblastoma. Most of the time, these tumours migrate to neighbouring brain tissue. Those with high grade glioma (GBM), which is highly aggressive and progresses quickly, have a poor survival rate compared to those with other tumours. Radiologist clinical decision-making and methodical treatment planning for patients can be enhanced by using survival time predictions (also known as OS time). Many imaging features of the brain, including the size and shape of the tumour, determine the outcome for the patient as a whole. In this paper, we used Random Forest, Support Vector Machines, XgBoost, and the Logistic Regression with Boosting Method (LGBM) to predict the overall survival (OS) period based on radiomic features. These radiomic characteristics are a combination of the tumor's deep characteristics and the characteristics that were shaped by hand. The prediction's reliability is dependent on the tumour volume being isolated from the various MRI modalities. Because of this, the U-Net++ deep model is used to recover the complete tumour and its subtumor from the multi-modal MR images, and then the pictures are stacked for deep feature extraction using convolutional neural networks. After feature reduction by principal component analysis (PCA) enhanced accuracy, the radiomic feature set was used for OS period forecasting. The accuracy of the forecast was examined utilising data from both two- and three-class survival analyses. An experiment was run using the popular BraTS 2017 dataset, and the findings indicated that several classifiers were able to reach an AUC value of 69% for a 3-class classification and a 67% AUC value for a 2-class group. The segmentation DOR is calculated to be 1269.29, which is greater than 2033.99 and lower than 648.00 for entire tumour, augmenting tumour, and necrotic tumour extraction, respectively. Both the genetic algorithm (GA) and the particle swarm optimisation (PSO) are used to the fused feature set to improve accuracy even further. Eventually, the approach achieves an area under the curve (AUC) score of 0.66 when employing fused features + SVM + GA (3-class group) and 0.70 when employing fused features + SVM + PSO, both of which are better than state-of-the-art methods (2-class group). Both of these results are higher than the minimum passing grade of 0.65.
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