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

Badža, Milica M.; Barjaktarović, Marko

doi:10.3390/app10061999

Cited by 395 publications

(203 citation statements)

References 38 publications

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“…This study achieved best overall accuracy of 96.13% on T1-weighted contrast-enhanced images without using fold cross-validation. Badža M. et al 2020 [ 29 ], proposed a new 22 layers CNN, using custom brain MRI scans dataset collected from Tianjing Medical University, China as well. This approach achieved an accuracy of 96.56% for classification of three tumor types tested on T1-weighted contrast-enhanced MRI.…”

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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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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“…Also, the scarcity of data is a concern relating to these kinds of work. Very recently, deep learning-based techniques [10,27] have been adopted to address these issues. Techniques such as transfer learning [28] have also been implemented to improve model performance.…”

Section: Related Workmentioning

confidence: 99%

“…Developing a model with a high accuracy is a challenging task. Recent version of CNN models [8][9][10] have hardly focused on hyper parameters whereas we do so; the collection [2] of features that are locally available to the CNN are also a critical issue; moreover bluntly increasing the dilation rate may add to the failure of feature collections due to the sparseness of the kernel, affecting small object detection [11]. High dilation rates may affect small object detection.…”

Section: Introductionmentioning

confidence: 99%

Incremental Dilations Using CNN for Brain Tumor Classification

2020

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Brain tumor classification is a challenging task in the field of medical image processing.Technology has now enabled medical doctors to have additional aid for diagnosis. We aim toclassify brain tumors using MRI images, which were collected from anonymous patients andartificial brain simulators . In this article, we carry out a comparative study between SimpleArtificial Neural Networks with dropout, Basic Convolutional Neural Networks (CNN), and DilatedConvolutional Neural Networks. The experimental results shed light on the high classificationperformance (accuracy 97%) of Dilated CNN. On the other hand, Dilated CNN suffers from thegridding phenomenon. An incremental, even number dilation rate takes advantage of the reducedcomputational overhead and also overcomes the adverse effects of gridding. Comparative analysisbetween different combinations of dilation rates for the different convolution layers, help validatethe results. The computational overhead in terms of efficiency for training the model to reach anacceptable threshold accuracy of 90% is another parameter to compare the model performance.

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“…Finally, he was made the accuracy of 91.28% for MRI image tumor classification. Badza et al [13] present a new CNN based model for brain tumor classification of three tumor types. The performance of the network is evaluated in four approaches in the combination of two 10-fold cross validations and two databases (original database and augmented database).…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Neural Networks with Transfer Learning Model for Brain Tumors Classification

Premamayudu¹,

Anantha²,

Subbarao³

2020

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To investigate the effect of deep neural networks with transfer learning on MR images for tumor classification and improve the classification metrics by building image-level, stratified image-level, and patient-level models. Three thousand sixty-four T1-weighted magnetic resonance (MR) imaging from two hundred thirty-three patient cases of three brain tumors types (meningioma, glioma, and pituitary) were collected and it includes coronal, sagittal and axial views. The average number of brain images of each patient in three views is fourteen in the collected dataset. The classification is performed in a model of cross-trained with a pre-trained InceptionV3 model. Three image-level and one patient-level models are built on the MR imaging dataset. The models are evaluated in classification metrics such as accuracy, loss, precision, recall, kappa, and AUC. The proposed models are validated using four approaches: holdout validation, 10-fold cross-validation, stratified 10-fold cross-validation, and group 10-fold cross-validation. The generalization capability and improvement of the network are tested by using cropped and uncropped images of the dataset. The best results for group 10-fold cross-validation (patient-level) are obtained on the used dataset (ACC=99.82). A deep neural network with transfer learning can be used to classify brain tumors from MR images. Our patient-level network model noted the best results in classification to improve accuracy.

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

Cited by 395 publications

References 38 publications

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

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

Incremental Dilations Using CNN for Brain Tumor Classification

Deep Neural Networks with Transfer Learning Model for Brain Tumors Classification

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