Brain Tumor Classification via Statistical Features and Back-Propagation Neural Network

Ismael, Mustafa; Abdel‐Qader, Ikhlas

doi:10.1109/eit.2018.8500308

Cited by 162 publications

(91 citation statements)

References 20 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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“…The study claimed to achieve an accuracy measure of 98.5% on training and 84.19% for validation. The authors in [21] employed a two-dimensional discrete transform based on wavelets and Gabor filters for extraction of features of brain MRI. The study achieved an accuracy measure of 91.9% by employing the aforementioned system setup with backpropagation NN.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Meningioma represented 36.3% (29,320), Gliomas 26.5% (21,200), Pituitary tumors represented nearly 16.2% (13,210) and rest of the cases belonged to other types of brain tumor such as Malignant, Medulloblastoma, and Lymphomas. The principal causes of such disease are cancer-related ailment and morbidity.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor

Noreen

Palaniappan

Qayyum³

et al. 2020

IEEE Access

260

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Brain tumor is a deadly disease and its classification is a challenging task for radiologists because of the heterogeneous nature of the tumor cells. Recently, computer-aided diagnosis-based systems have promised, as an assistive technology, to diagnose the brain tumor, through magnetic resonance imaging (MRI). In recent applications of pre-trained models, normally features are extracted from bottom layers which are different from natural images to medical images. To overcome this problem, this study proposes a method of multi-level features extraction and concatenation for early diagnosis of brain tumor. Two pretrained deep learning models i.e. Inception-v3 and DensNet201 make this model valid. With the help of these two models, two different scenarios of brain tumor detection and its classification were evaluated. First, the features from different Inception modules were extracted from pre-trained Inception-v3 model and concatenated these features for brain tumor classification. Then, these features were passed to softmax classifier to classify the brain tumor. Second, pre-trained DensNet201 was used to extract features from various DensNet blocks. Then, these features were concatenated and passed to softmax classifier to classify the brain tumor. Both scenarios were evaluated with the help of three-class brain tumor dataset that is available publicly. The proposed method produced 99.34 %, and 99.51% testing accuracies respectively with Inception-v3 and DensNet201 on testing samples and achieved highest performance in the detection of brain tumor. As results indicated, the proposed method based on features concatenation using pre-trained models outperformed as compared to existing state-of-the-art deep learning and machine learning based methods for brain tumor classification. INDEX TERMS Deep learning, magnetic resonance imaging, brain tumor classification, pre-trained model, dataset.

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“…In the process of brain disease diagnosis, firstly, the image features are extracted, and then the extracted image features are classified to complete the image classification and recognition. For example, Ismael and Abdel-Qader (2018) used Gabor filter and discrete wavelet transform to extract statistical features for brain tumor classification. Then this method used the tumor segmented as input and multi-layer perceptron (MLP) as the classifier.…”

Section: Introductionmentioning

confidence: 99%

A Transfer Model Based on Supervised Multi-Layer Dictionary Learning for Brain Tumor MRI Image Recognition

2021

Front. Neurosci.

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Artificial intelligence (AI) is an effective technology for automatic brain tumor MRI image recognition. The training of an AI model requires a large number of labeled data, but medical data needs to be labeled by professional clinicians, which makes data collection complex and expensive. Moreover, a traditional AI model requires that the training data and test data must follow the independent and identically distributed. To solve this problem, we propose a transfer model based on supervised multi-layer dictionary learning (TSMDL) for brain tumor MRI image recognition in this paper. With the help of the knowledge learned from related domains, the goal of this model is to solve the task of transfer learning where the target domain has only a small number of labeled samples. Based on the framework of multi-layer dictionary learning, the proposed model learns the common shared dictionary of source and target domains in each layer to explore the intrinsic connections and shared information between different domains. At the same time, by making full use of the label information of samples, the Laplacian regularization term is introduced to make the dictionary coding of similar samples as close as possible and the dictionary coding of different class samples as different as possible. The recognition experiments on brain MRI image datasets REMBRANDT and Figshare show that the model performs better than competitive state of-the-art methods.

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Brain Tumor Classification via Statistical Features and Back-Propagation Neural Network

Cited by 162 publications

References 20 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

A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor

A Transfer Model Based on Supervised Multi-Layer Dictionary Learning for Brain Tumor MRI Image Recognition

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