A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network

Pernas, Francisco Javier Díaz; Martínez‐Zarzuela, Mario; Antón-Rodríguez, M.; González-Ortega, D.

doi:10.3390/healthcare9020153

Cited by 237 publications

(97 citation statements)

References 29 publications

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“…Using 5-fold cross-validation, average accuracy achieved by the proposed technique is 91.46%. A multiscale Deep CNN [ 29 ] is proposed which can analyse tumor MRIs and classify them into glioma, meningioma, and pituitary tumor. The performance of the proposed model is evaluated on an MRI image dataset consisting of 3,064 images.…”

Section: Related Workmentioning

confidence: 99%

Computer-Aided Brain Tumor Diagnosis: Performance Evaluation of Deep Learner CNN Using Augmented Brain MRI

Naseer

Yasir

Azhar

et al. 2021

International Journal of Biomedical Imaging

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Brain tumor is a deadly neurological disease caused by an abnormal and uncontrollable growth of cells inside the brain or skull. The mortality ratio of patients suffering from this disease is growing gradually. Analysing Magnetic Resonance Images (MRIs) manually is inadequate for efficient and accurate brain tumor diagnosis. An early diagnosis of the disease can activate a timely treatment consequently elevating the survival ratio of the patients. Modern brain imaging methodologies have augmented the detection ratio of brain tumor. In the past few years, a lot of research has been carried out for computer-aided diagnosis of human brain tumor to achieve 100% diagnosis accuracy. The focus of this research is on early diagnosis of brain tumor via Convolution Neural Network (CNN) to enhance state-of-the-art diagnosis accuracy. The proposed CNN is trained on a benchmark dataset, BR35H, containing brain tumor MRIs. The performance and sustainability of the model is evaluated on six different datasets, i.e., BMI-I, BTI, BMI-II, BTS, BMI-III, and BD-BT. To improve the performance of the model and to make it sustainable for totally unseen data, different geometric data augmentation techniques, along with statistical standardization, are employed. The proposed CNN-based CAD system for brain tumor diagnosis performs better than other systems by achieving an average accuracy of around 98.8% and a specificity of around 0.99. It also reveals 100% correct diagnosis for two brain MRI datasets, i.e., BTS and BD-BT. The performance of the proposed system is also compared with the other existing systems, and the analysis reveals that the proposed system outperforms all of them.

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Section: Related Workmentioning

confidence: 99%

Computer-Aided Brain Tumor Diagnosis: Performance Evaluation of Deep Learner CNN Using Augmented Brain MRI

Naseer

Yasir

Azhar

et al. 2021

International Journal of Biomedical Imaging

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“…In this section, we compare our proposed model with the other segmentation techniques using the Figshare brain tumor dataset [ 38 ] which is one of the largest online available datasets. For performance evaluation, we compared the average highest results of our presented technique with the average results reported in these studies [ 61 , 62 , 63 , 64 , 65 ]. For the presented technique, we have shown the results for the DenseNet-41-based Mask-RCNN framework as we obtained better performance on it as compared to the ResNet-50 framework.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…However, the authors considered only the axial MR images of glioma and meningioma brain tumors. In [ 64 ], the authors proposed a multi-scale CNN model that processes the input MR image in three different spatial scales using multiple processing pathways. They achieved an average accuracy of 0.973 and a dice score of 0.828 for classification and segmentation, respectively.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…The proposed technique uses deep features that are more discriminating, reliable, and provide a more effective representation of tumor regions over other methods such as [ 61 ], which employs the hand-crafted features and is unable to better represent the tumor region due to structural complexities. Moreover, in some existing methods [ 62 , 64 ] segmentation is applied directly to the entire image, which results in misclassification due to the complex background (i.e., brain tissues overlapping with tumor boundary, MRI artifacts, etc. ), which thus reduces the accuracy of the segmentation.…”

Section: Performance Evaluationmentioning

confidence: 99%

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A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images

et al. 2021

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A brain tumor is an abnormal growth in brain cells that causes damage to various blood vessels and nerves in the human body. An earlier and accurate diagnosis of the brain tumor is of foremost important to avoid future complications. Precise segmentation of brain tumors provides a basis for surgical planning and treatment to doctors. Manual detection using MRI images is computationally complex in cases where the survival of the patient is dependent on timely treatment, and the performance relies on domain expertise. Therefore, computerized detection of tumors is still a challenging task due to significant variations in their location and structure, i.e., irregular shapes and ambiguous boundaries. In this study, we propose a custom Mask Region-based Convolution neural network (Mask RCNN) with a densenet-41 backbone architecture that is trained via transfer learning for precise classification and segmentation of brain tumors. Our method is evaluated on two different benchmark datasets using various quantitative measures. Comparative results show that the custom Mask-RCNN can more precisely detect tumor locations using bounding boxes and return segmentation masks to provide exact tumor regions. Our proposed model achieved an accuracy of 96.3% and 98.34% for segmentation and classification respectively, demonstrating enhanced robustness compared to state-of-the-art approaches.

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“…2021, 11, 5118 2 of 11 CNN is being developed. Abd El Kader et al [8] proposed a differential deep CNN model to classify abnormal or normal MR brain images, and Díaz-Pernas et al [9] proposed a multiscale CNN model for region extraction and classification of three types of brain tumors, including glioma, in post-contrast T1-weighted images. However, in these studies, 3D MR images were analyzed on a slice-by-slice basis.…”

Section: Introductionmentioning

confidence: 99%

Development of a Fully Automated Glioma-Grading Pipeline Using Post-Contrast T1-Weighted Images Combined with Cloud-Based 3D Convolutional Neural Network

2021

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Glioma is the most common type of brain tumor, and its grade influences its treatment policy and prognosis. Therefore, artificial-intelligence-based tumor grading methods have been studied. However, in most studies, two-dimensional (2D) analysis and manual tumor-region extraction were performed. Additionally, deep learning research that uses medical images experiences difficulties in collecting image data and preparing hardware, thus hindering its widespread use. Therefore, we developed a 3D convolutional neural network (3D CNN) pipeline for realizing a fully automated glioma-grading system by using the pretrained Clara segmentation model provided by NVIDIA and our original classification model. In this method, the brain tumor region was extracted using the Clara segmentation model, and the volume of interest (VOI) created using this extracted region was assigned to a grading 3D CNN and classified as either grade II, III, or IV. Through evaluation using 46 regions, the grading accuracy of all tumors was 91.3%, which was comparable to that of the method using multi-sequence. The proposed pipeline scheme may enable the creation of a fully automated glioma-grading pipeline in a single sequence by combining the pretrained 3D CNN and our original 3D CNN.

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A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network

Cited by 237 publications

References 29 publications

Computer-Aided Brain Tumor Diagnosis: Performance Evaluation of Deep Learner CNN Using Augmented Brain MRI

Computer-Aided Brain Tumor Diagnosis: Performance Evaluation of Deep Learner CNN Using Augmented Brain MRI

A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images

Development of a Fully Automated Glioma-Grading Pipeline Using Post-Contrast T1-Weighted Images Combined with Cloud-Based 3D Convolutional Neural Network

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