Multi-Classification of Brain Tumor Images Using Deep Neural Network

Sultan, Hossam H.; Salem, Nancy M.; Al-Atabany, Walid

doi:10.1109/access.2019.2919122

Cited by 477 publications

(216 citation statements)

References 30 publications

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“…A comparison with the studies that used designed neural networks and an augmented dataset, but did not test it by k-fold cross-validation, is presented in Table 5. In the literature, there are also studies that used the same database for classification with pre-trained networks [23,[35][36][37][38][39][40] or, as input, they use only tumor region or some features that are extracted from the tumor region [7,21,23,41,42]. Similarly, in several papers, researchers have modified this database prior to classification [36,[43][44][45][46][47].…”

Section: Comparison With State-of-the-art-methodsmentioning

confidence: 99%

Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network

2020

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The classification of brain tumors is performed by biopsy, which is not usually conducted before definitive brain surgery. The improvement of technology and machine learning can help radiologists in tumor diagnostics without invasive measures. A machine-learning algorithm that has achieved substantial results in image segmentation and classification is the convolutional neural network (CNN). We present a new CNN architecture for brain tumor classification of three tumor types. The developed network is simpler than already-existing pre-trained networks, and it was tested on T1-weighted contrast-enhanced magnetic resonance images. The performance of the network was evaluated using four approaches: combinations of two 10-fold cross-validation methods and two databases. The generalization capability of the network was tested with one of the 10-fold methods, subject-wise cross-validation, and the improvement was tested by using an augmented image database. The best result for the 10-fold cross-validation method was obtained for the record-wise cross-validation for the augmented data set, and, in that case, the accuracy was 96.56%. With good generalization capability and good execution speed, the new developed CNN architecture could be used as an effective decision-support tool for radiologists in medical diagnostics.

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Section: Comparison With State-of-the-art-methodsmentioning

confidence: 99%

Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network

2020

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“…Their experiments were performed using a large number of features for MR brain tumor slices. Sultan, H et al 2019 [ 28 ], proposed a new CNN model with 16 layers using custom brain MRI scans dataset collected from Tianjing Medical University, China. This study achieved best overall accuracy of 96.13% on T1-weighted contrast-enhanced images without using fold cross-validation.…”

Section: Resultsmentioning

confidence: 99%

MRI Brain Classification Using the Quantum Entropy LBP and Deep-Learning-Based Features

Hasan

Jalab

Ibrahim

et al. 2020

Entropy

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Brain tumor detection at early stages can increase the chances of the patient’s recovery after treatment. In the last decade, we have noticed a substantial development in the medical imaging technologies, and they are now becoming an integral part in the diagnosis and treatment processes. In this study, we generalize the concept of entropy difference defined in terms of Marsaglia formula (usually used to describe two different figures, statues, etc.) by using the quantum calculus. Then we employ the result to extend the local binary patterns (LBP) to get the quantum entropy LBP (QELBP). The proposed study consists of two approaches of features extractions of MRI brain scans, namely, the QELBP and the deep learning DL features. The classification of MRI brain scan is improved by exploiting the excellent performance of the QELBP–DL feature extraction of the brain in MRI brain scans. The combining all of the extracted features increase the classification accuracy of long short-term memory network when using it as the brain tumor classifier. The maximum accuracy achieved for classifying a dataset comprising 154 MRI brain scan is 98.80%. The experimental results demonstrate that combining the extracted features improves the performance of MRI brain tumor classification.

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“…An improved output is obtained due to the improved back propagation. In [22], a 3 CNN layer-based deep learning method was proposed to classify different brain tumour types and grades. Deep-CNN-based transfer learning and fine-tuning was used to segment brain tumors [23].…”

Section: Literature Reviewmentioning

confidence: 99%

State-of-the-Art CNN Optimizer for Brain Tumor Segmentation in Magnetic Resonance Images

et al. 2020

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Brain tumors have become a leading cause of death around the globe. The main reason for this epidemic is the difficulty conducting a timely diagnosis of the tumor. Fortunately, magnetic resonance images (MRI) are utilized to diagnose tumors in most cases. The performance of a Convolutional Neural Network (CNN) depends on many factors (i.e., weight initialization, optimization, batches and epochs, learning rate, activation function, loss function, and network topology), data quality, and specific combinations of these model attributes. When we deal with a segmentation or classification problem, utilizing a single optimizer is considered weak testing or validity unless the decision of the selection of an optimizer is backed up by a strong argument. Therefore, optimizer selection processes are considered important to validate the usage of a single optimizer in order to attain these decision problems. In this paper, we provides a comprehensive comparative analysis of popular optimizers of CNN to benchmark the segmentation for improvement. In detail, we perform a comparative analysis of 10 different state-of-the-art gradient descent-based optimizers, namely Adaptive Gradient (Adagrad), Adaptive Delta (AdaDelta), Stochastic Gradient Descent (SGD), Adaptive Momentum (Adam), Cyclic Learning Rate (CLR), Adaptive Max Pooling (Adamax), Root Mean Square Propagation (RMS Prop), Nesterov Adaptive Momentum (Nadam), and Nesterov accelerated gradient (NAG) for CNN. The experiments were performed on the BraTS2015 data set. The Adam optimizer had the best accuracy of 99.2% in enhancing the CNN ability in classification and segmentation.

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Multi-Classification of Brain Tumor Images Using Deep Neural Network

Cited by 477 publications

References 30 publications

Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network

Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network

MRI Brain Classification Using the Quantum Entropy LBP and Deep-Learning-Based Features

State-of-the-Art CNN Optimizer for Brain Tumor Segmentation in Magnetic Resonance Images

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