Brain imaging classification based On Learning Vector Quantization

Nayef, Bahera H.; Sahran, Shahnorbanun; Hussain, Rizuana Iqbal; Abdullah, Siti Norul Huda Sheikh

doi:10.1109/iccspa.2013.6487253

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…The proposed RBEBT is compared with seven other state-of-the-art methods, which are 3D-CNN [1], SVM-CNN [2], KNN-CNN [5], 2D-CNN [7], BPNN [11], LVQNN [12], and LRC [13], as shown in Tab. 2.…”

Section: Comparison With Other State-of-the-art Methodsmentioning

confidence: 99%

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RBEBT: A ResNet-Based BA-ELM for Brain Tumor Classification

Zhu¹,

Khan²,

Wang³

et al. 2023

Computers, Materials &Amp; Continua

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Brain tumor refers to the formation of abnormal cells in the brain. It can be divided into benign and malignant. The main diagnostic methods for brain tumors are plain X-ray film, Magnetic resonance imaging (MRI), and so on. However, these artificial diagnosis methods are easily affected by external factors. Scholars have made such impressive progress in brain tumors classification by using convolutional neural network (CNN). However, there are still some problems: (i) There are many parameters in CNN, which require much calculation. (ii) The brain tumor data sets are relatively small, which may lead to the overfitting problem in CNN. In this paper, our team proposes a novel model (RBEBT) for the automatic classification of brain tumors. We use fine-tuned ResNet18 to extract the features of brain tumor images. The RBEBT is different from the traditional CNN models in that the randomized neural network (RNN) is selected as the classifier. Meanwhile, our team selects the bat algorithm (BA) to optimize the parameters of RNN. We use fivefold cross-validation to verify the superiority of the RBEBT. The accuracy (ACC), specificity (SPE), precision (PRE), sensitivity (SEN), and F1-score (F1) are 99.00%, 95.00%, 99.00%, 100.00%, and 100.00%. The classification performance of the RBEBT is greater than 95%, which can prove that the RBEBT is an effective model to classify brain tumors.

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Section: Comparison With Other State-of-the-art Methodsmentioning

confidence: 99%

“…Hemanth et al [11] proposed the BPNN for the segmentation of brain tumors images. Nayef et al [12] introduced a new method (LVQNN) to classify brain tumors. Chen et al [13] proposed the LRC to classify brain tumors.…”

Section: Introductionmentioning

confidence: 99%

RBEBT: A ResNet-Based BA-ELM for Brain Tumor Classification

Zhu¹,

Khan²,

Wang³

et al. 2023

Computers, Materials &Amp; Continua

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“…We compare the proposed DSNN with other state-of-the-art methods. These state-of-the-art methods are: ANN (Arunkumar et al, 2020), PR2G (Kalaiselvi et al, 2020), SRH + CNNs (Hollon et al, 2020), BPNN (Hemanth et al, 2011), LVQNN (Nayef et al, 2013), and LRC (Chen et al, 2017), respectively. The results are presented in Table 8.…”

Section: Comparison With Other State-of-the-art Methodsmentioning

confidence: 99%

DSNN: A DenseNet-Based SNN for Explainable Brain Disease Classification

Zhu

Wang

et al. 2022

Front. Syst. Neurosci.

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Aims: Brain diseases refer to intracranial tissue and organ inflammation, vascular diseases, tumors, degeneration, malformations, genetic diseases, immune diseases, nutritional and metabolic diseases, poisoning, trauma, parasitic diseases, etc. Taking Alzheimer’s disease (AD) as an example, the number of patients dramatically increases in developed countries. By 2025, the number of elderly patients with AD aged 65 and over will reach 7.1 million, an increase of nearly 29% over the 5.5 million patients of the same age in 2018. Unless medical breakthroughs are made, AD patients may increase from 5.5 million to 13.8 million by 2050, almost three times the original. Researchers have focused on developing complex machine learning (ML) algorithms, i.e., convolutional neural networks (CNNs), containing millions of parameters. However, CNN models need many training samples. A small number of training samples in CNN models may lead to overfitting problems. With the continuous research of CNN, other networks have been proposed, such as randomized neural networks (RNNs). Schmidt neural network (SNN), random vector functional link (RVFL), and extreme learning machine (ELM) are three types of RNNs.Methods: We propose three novel models to classify brain diseases to cope with these problems. The proposed models are DenseNet-based SNN (DSNN), DenseNet-based RVFL (DRVFL), and DenseNet-based ELM (DELM). The backbone of the three proposed models is the pre-trained “customize” DenseNet. The modified DenseNet is fine-tuned on the empirical dataset. Finally, the last five layers of the fine-tuned DenseNet are substituted by SNN, ELM, and RVFL, respectively.Results: Overall, the DSNN gets the best performance among the three proposed models in classification performance. We evaluate the proposed DSNN by five-fold cross-validation. The accuracy, sensitivity, specificity, precision, and F1-score of the proposed DSNN on the test set are 98.46% ± 2.05%, 100.00% ± 0.00%, 85.00% ± 20.00%, 98.36% ± 2.17%, and 99.16% ± 1.11%, respectively. The proposed DSNN is compared with restricted DenseNet, spiking neural network, and other state-of-the-art methods. Finally, our model obtains the best results among all models.Conclusions: DSNN is an effective model for classifying brain diseases.

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