Brain Tumor Classification Using Deep Learning

2020 ◽  
Author(s):  
Ahmad Saleh ◽  
Rozana Sukaik ◽  
Samy S. Abu-Naser
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Tumor Classification

scholarly journals Deep learning for brain tumor classification

2017 ◽  
Author(s):  
Justin S. Paul ◽  
Andrew J. Plassard ◽  
Bennett A. Landman ◽  
Daniel Fabbri
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Tumor Classification

scholarly journals Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Linmin Pei ◽  
Lasitha Vidyaratne ◽  
Md Monibor Rahman ◽  
Khan M. Iftekharuddin
Keyword(s):  
Overall Survival ◽  
Deep Learning ◽  
Brain Tumor ◽  
Tumor Classification ◽  
Tumor Segmentation ◽  
Survival Prediction ◽  
Context Aware ◽  
Brain Tumor Segmentation ◽  
Subtype Classification ◽  
Overall Survival Prediction

AbstractA brain tumor is an uncontrolled growth of cancerous cells in the brain. Accurate segmentation and classification of tumors are critical for subsequent prognosis and treatment planning. This work proposes context aware deep learning for brain tumor segmentation, subtype classification, and overall survival prediction using structural multimodal magnetic resonance images (mMRI). We first propose a 3D context aware deep learning, that considers uncertainty of tumor location in the radiology mMRI image sub-regions, to obtain tumor segmentation. We then apply a regular 3D convolutional neural network (CNN) on the tumor segments to achieve tumor subtype classification. Finally, we perform survival prediction using a hybrid method of deep learning and machine learning. To evaluate the performance, we apply the proposed methods to the Multimodal Brain Tumor Segmentation Challenge 2019 (BraTS 2019) dataset for tumor segmentation and overall survival prediction, and to the dataset of the Computational Precision Medicine Radiology-Pathology (CPM-RadPath) Challenge on Brain Tumor Classification 2019 for tumor classification. We also perform an extensive performance evaluation based on popular evaluation metrics, such as Dice score coefficient, Hausdorff distance at percentile 95 (HD95), classification accuracy, and mean square error. The results suggest that the proposed method offers robust tumor segmentation and survival prediction, respectively. Furthermore, the tumor classification results in this work is ranked at second place in the testing phase of the 2019 CPM-RadPath global challenge.

scholarly journals A Transfer Learning–Based Active Learning Framework for Brain Tumor Classification

2021 ◽  
Vol 4 ◽  
Author(s):  
Ruqian Hao ◽  
Khashayar Namdar ◽  
Lin Liu ◽  
Farzad Khalvati
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Active Learning ◽  
Transfer Learning ◽  
Medical Images ◽  
Tumor Classification ◽  
Training Data ◽  
Validation Dataset ◽  
Low Grade ◽  
Learning Framework

Brain tumor is one of the leading causes of cancer-related death globally among children and adults. Precise classification of brain tumor grade (low-grade and high-grade glioma) at an early stage plays a key role in successful prognosis and treatment planning. With recent advances in deep learning, artificial intelligence–enabled brain tumor grading systems can assist radiologists in the interpretation of medical images within seconds. The performance of deep learning techniques is, however, highly depended on the size of the annotated dataset. It is extremely challenging to label a large quantity of medical images, given the complexity and volume of medical data. In this work, we propose a novel transfer learning–based active learning framework to reduce the annotation cost while maintaining stability and robustness of the model performance for brain tumor classification. In this retrospective research, we employed a 2D slice–based approach to train and fine-tune our model on the magnetic resonance imaging (MRI) training dataset of 203 patients and a validation dataset of 66 patients which was used as the baseline. With our proposed method, the model achieved area under receiver operating characteristic (ROC) curve (AUC) of 82.89% on a separate test dataset of 66 patients, which was 2.92% higher than the baseline AUC while saving at least 40% of labeling cost. In order to further examine the robustness of our method, we created a balanced dataset, which underwent the same procedure. The model achieved AUC of 82% compared with AUC of 78.48% for the baseline, which reassures the robustness and stability of our proposed transfer learning augmented with active learning framework while significantly reducing the size of training data.

scholarly journals Hybrid Architecture for Brain Abnormality Detection on brain MR Scans via FastAI

2021 ◽  
Author(s):  
Asmita Dixit
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Transfer Learning ◽  
Tumor Classification ◽  
Tumor Segmentation ◽  
Hybrid Architecture ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Brain Abnormality ◽  
Computer Aided

Abstract With lot happening in the field of Deep Learning, classification of brain tumor is still a matter of concern. Brain tumor segmentation and classification using MRI scans has achieved lot of interest in the area of medical imaging. The emphasis still lies on developing automatic computer-aided system for early predictions and diagnosis. MRI of brain Tumors not only varies in shape but sometimes gives less contrasted details also. In this paper, we present a FastAI based Transfer Learning tumor classification in which pre-trained model with segmented features classifies tumor based on its learning. The proposed model with the technique of Deep learning applies ResNet152 as base model to extract features from the MRI brain images. With certain changes in the last 3 layers of ResNet152, 97% accuracy in Dataset-253, 96% accuracy in Dataset-205 is achieved. Models such as Resnet50, VGG16, ResNet34 and Basic CNN is also evaluated. The model improved from ResNet152 has provided improved results. The observations suggest that usage of Transfer Learning is effective when the Dataset is limited. The prepared model is effective and can be collaborated in computer-aided brain MR images Tumor classification.

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