Machine Learning and Deep Learning Techniques to Predict Overall Survival of Brain Tumor Patients using MRI Images

Chato, Lina; Latifi, Shahram

doi:10.1109/bibe.2017.00-86

Cited by 60 publications

(35 citation statements)

References 14 publications

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“…Table IV compares the accuracy of the proposed method with other state-of-the-art techniques. Methods proposed in [13], [14] uses the dataset of BraTS 2017 whereas other methods [19]- [22], [25], [26] use BraTS 2018 dataset. In [19], classifier training set is made up of the images with GTR status.…”

Section: Resultsmentioning

confidence: 99%

“…Classifier(s) Accuracy% [13] Ensemble of random forest and multi layer perceptron 52.6 [14] Linear Discriminant 46 [19] Linear SVM GTR set 63 [20] Neural network and random forest 38 [21] Artificial neural network 54.5 [22] Multi layer perceptron 50.8 [25] XGBoost 65 [26] Ensemble of random forest and regression network 47.5…”

Section: Refmentioning

confidence: 99%

“…In another attempt at survival prediction [14], the authors use pre-trained AlexNet to segment the brain tumor. The features from segmentions are used to train the linear discriminant for survival prediction.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Overall Survival of Brain Tumor Patients

Agravat

Raval

2019

TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)

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Automated brain tumor segmentation plays an important role in the diagnosis and prognosis of the patient. In addition, features from the tumorous brain help in predicting patients' overall survival. The main focus of this paper is to segment tumor from BRATS 2018 benchmark dataset and use age, shape and volumetric features to predict overall survival of patients. The random forest classifier achieves overall survival accuracy of 59% on the test dataset and 67% on the dataset with resection status as gross total resection. The proposed approach uses fewer features but achieves better accuracy than state-ofthe-art methods.

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Section: Resultsmentioning

confidence: 99%

Section: Refmentioning

confidence: 99%

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Prediction of Overall Survival of Brain Tumor Patients

Agravat

Raval

2019

TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)

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“…Different ML and DL methods that include Support Vector Machine (SVM), e, linear discriminant analysis, logistic regression and K-Nearest Neighbors (KNN) are tested on Brat's dataset, and accuracies are compared. The best prediction accuracy is achieved using a hybrid algorithm combining CNN and linear discriminant analysis [87]. CNN is a well-known method for image recognition and prediction.…”

Section: Brain Tumor Predictionmentioning

confidence: 99%

Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

et al. 2020

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Deep Learning (DL) algorithms enabled computational models consist of multiple processing layers that represent data with multiple levels of abstraction. In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics. Consequently, deep learning has dramatically changed and improved the means of recognition, prediction, and diagnosis effectively in numerous areas of healthcare such as pathology, brain tumor, lung cancer, abdomen, cardiac, and retina. Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification, prediction, evaluation.). A review conducted by summarizing a large number of scientific contributions to the field (i.e., deep learning in brain tumor analysis) is presented in this study. A coherent taxonomy of research landscape from the literature has also been mapped, and the major aspects of this emerging field have been discussed and analyzed. A critical discussion section to show the limitations of deep learning techniques has been included at the end to elaborate open research challenges and directions for future work in this emergent area.

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“…In the third category, hybrid approaches, deep features that are extracted using DL methods, handcrafted features that are extracted from automatic segmented tumor regions, and clinical data are combined to create a feature fusion matrix. This matrix is then used as input to train ML algorithms [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Ensemble learning of multiview CNN models for survival time prediction of brain tumor patients using multimodal MRI scans

A¹,

Çevik²

2021

Turk J Elec Eng & Comp Sci

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Brain tumors have been one of the most common life-threatening diseases for all mankind. There have been huge efforts dedicated to the development of medical imaging techniques and radiomics to diagnose tumor patients quickly and efficiently. One of the main aims is to ensure that pre-operative overall survival time (OS) prediction is accurate. Recently, deep learning (DL) algorithms, and particularly convolutional neural networks (CNNs) achieved promising performances in almost all computer vision fields. CNNs demand large training datasets and high computational costs. However, curating large annotated medical datasets are difficult and resource-intensive. The performances of single learners are also unsatisfactory for small datasets. Thus, this study was conducted to improve the performance of CNN models on small volumetric datasets through developing a DL-based ensemble method for OS classification of brain tumor patients using multi-modal magnetic resonance images (MRI). First, we proposed Multi-View CNNs: OS classifiers based on representing the 3D MRI data as a set of 2D slices along all three planes (axial, sagittal, and coronal) and process them using 2D CNNs. Subsequently, the predicted probabilities by the Multi-View CNN models were fused using standard machine learning algorithms. The proposed approach was experimentally evaluated on 163 patients obtained from the BraTS'17 training dataset. Our best model achieved an AUC and accuracy values of 0.93 and 92.9%, respectively, on classifying patients with brain tumors into two OS groups, outperforming current state-of-the-art results. In addition, the FLAIR MRI modality yielded the best classification accuracy compared to other MRI modalities. Similarly, axial projections had the best classification performance compared to coronal and sagittal projections. Our findings may provide valuable insights for physicians in advancing treatment planning via noninvasive and accurate prediction of survival using only MRIs at the time of diagnosis.

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Machine Learning and Deep Learning Techniques to Predict Overall Survival of Brain Tumor Patients using MRI Images

Cited by 60 publications

References 14 publications

Prediction of Overall Survival of Brain Tumor Patients

Prediction of Overall Survival of Brain Tumor Patients

Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

Ensemble learning of multiview CNN models for survival time prediction of brain tumor patients using multimodal MRI scans

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