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

Pei, Linmin; Vidyaratne, Lasitha; Rahman, Monibor; Iftekharuddin, Khan M.

doi:10.1038/s41598-020-74419-9

Cited by 107 publications

(79 citation statements)

References 34 publications

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“…This research will be further expanded in future for the study of brain tumors using algorithms of quantum computation. [36] 2019 0.89 [39] 0.59 [40] 0.82 Proposed Method 0.96 [38] 2020 0.80 [57] 0.80 [34] 0.84 [27] 0.81 Proposed Method 0.97…”

Section: Resultsmentioning

confidence: 99%

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An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network

Sharif

Amin

et al. 2021

Complex Intell. Syst.

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Brain tumor is a group of anomalous cells. The brain is enclosed in a more rigid skull. The abnormal cell grows and initiates a tumor. Detection of tumor is a complicated task due to irregular tumor shape. The proposed technique contains four phases, which are lesion enhancement, feature extraction and selection for classification, localization, and segmentation. The magnetic resonance imaging (MRI) images are noisy due to certain factors, such as image acquisition, and fluctuation in magnetic field coil. Therefore, a homomorphic wavelet filer is used for noise reduction. Later, extracted features from inceptionv3 pre-trained model and informative features are selected using a non-dominated sorted genetic algorithm (NSGA). The optimized features are forwarded for classification after which tumor slices are passed to YOLOv2-inceptionv3 model designed for the localization of tumor region such that features are extracted from depth-concatenation (mixed-4) layer of inceptionv3 model and supplied to YOLOv2. The localized images are passed to McCulloch's Kapur entropy method to segment actual tumor region. Finally, the proposed technique is validated on three benchmark databases BRATS 2018, BRATS 2019, and BRATS 2020 for tumor detection. The proposed method achieved greater than 0.90 prediction scores in localization, segmentation and classification of brain lesions. Moreover, classification and segmentation outcomes are superior as compared to existing methods.

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Section: Resultsmentioning

confidence: 99%

“…The results comparison is given in Table 13. The proposed segmented results are compared with eight recent published works, such as [27,34,36,38,40,57,62]…”

Section: Experiment#4mentioning

confidence: 99%

An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network

Sharif

Amin

et al. 2021

Complex Intell. Syst.

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“…We also used the BraTS 2019 dataset as the BraTS dataset, including T1, T2, FLAIR, and GdT1 images of 259 high-grade glioma cases and 76 low-grade glioma cases with tumor volume of interest (VOI) information [16,30,31]. Further details regarding the BraTS dataset can be found elsewhere [16,30].…”

Section: Brats Datasetmentioning

confidence: 99%

Fine-Tuning Approach for Segmentation of Gliomas in Brain Magnetic Resonance Images with a Machine Learning Method to Normalize Image Differences among Facilities

Takahashi

Takahashi²,

Kinoshita

et al. 2021

Cancers

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Machine learning models for automated magnetic resonance image segmentation may be useful in aiding glioma detection. However, the image differences among facilities cause performance degradation and impede detection. This study proposes a method to solve this issue. We used the data from the Multimodal Brain Tumor Image Segmentation Benchmark (BraTS) and the Japanese cohort (JC) datasets. Three models for tumor segmentation are developed. In our methodology, the BraTS and JC models are trained on the BraTS and JC datasets, respectively, whereas the fine-tuning models are developed from the BraTS model and fine-tuned using the JC dataset. Our results show that the Dice coefficient score of the JC model for the test portion of the JC dataset was 0.779 ± 0.137, whereas that of the BraTS model was lower (0.717 ± 0.207). The mean Dice coefficient score of the fine-tuning model was 0.769 ± 0.138. There was a significant difference between the BraTS and JC models (p < 0.0001) and the BraTS and fine-tuning models (p = 0.002); however, no significant difference between the JC and fine-tuning models (p = 0.673). As our fine-tuning method requires fewer than 20 cases, this method is useful even in a facility where the number of glioma cases is small.

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“…This inspects quantitative features of brain tumors such as signal intensity shape and texture to forecast clinical outcomes such as response to therapy and survival. Linmin Pei [13] proposed a deep learning method for brain tumor classification and overall constancy prediction based on structural multimodal magnetic resonance images (mM-RIs). They first suggested a 3D context-aware deep learning that considers tumor area uncertainty in the radiology mMRI image sub-zones to perform tumor classification.…”

Section: Introductionmentioning

confidence: 99%

Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

et al. 2021

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The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

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

Cited by 107 publications

References 34 publications

An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network

An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network

Fine-Tuning Approach for Segmentation of Gliomas in Brain Magnetic Resonance Images with a Machine Learning Method to Normalize Image Differences among Facilities

Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

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