Brain Tumor Classification via Convolutional Neural Network and Extreme Learning Machines

Pashaei, Ali; Sajedi, Hedieh; Jazayeri, Niloofar

doi:10.1109/iccke.2018.8566571

Cited by 158 publications

(76 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other problems are feature redundancy, having noises and irrelevant data, which cause many troubles in prediction. Studies in [4][5][6][7] took advantage from ELM features.…”

Section: Related Workmentioning

confidence: 99%

“…ELM over performed other classification algorithms in some applications [5]. In [5] authors presented a method for classification of three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor) to do so Convolutional Neural Network (CNN) with four convolution layers, four pooling layers and one fully connected layer was used for feature extraction. Then Kernel based ELM was used for classify these features and CNN-KELM had promising…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Breast cancer diagnosis based on genomic data and extreme learning machine

Jazayeri

Sajedi

2019

SN Appl. Sci.

Self Cite

View full text Add to dashboard Cite

According to cancer.org news in 2018, the most common cancer diagnosed in women is breast, lung, and colorectal cancers. About 30% of all new cancer diagnoses in women refer to breast cancer. Therefore, predicting breast cancer in its early stages stays a controversial challenge. In this study, we focus on DNA methylation gene expression profiles of patients (series GSE32393 NCBI dataset). For dimension reduction, Non-negative matrix factorization (NMF) is employed and combined with a new method called column-splitting. The reason of superiority of NMF algorithm over other popular ones is its usage for both supervised and unsupervised feature matrix transformation into lower dimensional feature matrix. Afterward the main algorithms that are used for classification are extreme learning machine and support vector machine algorithms. The achieved prediction performances are comparable to deep learning models. The best-attained performance has zero error rate on NCBI 137 samples. While it has shown that, the best deep model on the mentioned data has 2.7 error rate.

show abstract

“…Other problems are feature redundancy, having noises and irrelevant data, which cause many troubles in prediction. Studies in [4][5][6][7] took advantage from ELM features.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Breast cancer diagnosis based on genomic data and extreme learning machine

Jazayeri

Sajedi

2019

SN Appl. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Proposed model performance metrics, accuracy and loss history of cropped images (a1,a2,a3) holdout validation (b1,b2,b3) 10-fold cross validation(image-level) (c1,c2,c3) stratified 10-fold cross validation (image-level) (d1,d2,d3) group 10fold cross validation ( patient-level) Figure 8. Proposed model performance metrics, accuracy and loss history of uncropped images (a1,a2,a3) holdout validation (b1,b2,b3) 10-fold cross validation(image-level) (c1,c2,c3) stratified 10-fold cross validation (image-level) (d1,d2,d3) group 10fold cross validation ( patient-level) [3] 86.56 ----Phaye et al [17] 95.03 ----Sultan et al [21] 96.13 96.06 94.43 --Gumaei et al [22] 92.16 ----Pashaei et al [25] 93 This section presents the comparison of the best performed transferred DCNN models with existing state-of-the-art works on the same dataset for brain tumor classification. To compare our findings with those of previous studies, we selected only those papers that built a neural network based on DCNN, used whole images as classification inputs and checked their networks using holdout and k-fold cross validation methods, as shown in Table 4.…”

Section: Resultsmentioning

confidence: 99%

Deep Neural Networks with Transfer Learning Model for Brain Tumors Classification

Premamayudu¹,

Anantha²,

Subbarao³

2020

View full text Add to dashboard Cite

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.

show abstract

“…Pashaei et al, [22] developed an architecture based on CNN for features extraction. They also designed a 5 layered architecture having all layers as learnable layers with customized 3 × 3 layered setup.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Noreen

Palaniappan

Qayyum³

et al. 2020

IEEE Access

248

View full text Add to dashboard Cite

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.

show abstract

Brain Tumor Classification via Convolutional Neural Network and Extreme Learning Machines

Cited by 158 publications

References 12 publications

Breast cancer diagnosis based on genomic data and extreme learning machine

Breast cancer diagnosis based on genomic data and extreme learning machine

Deep Neural Networks with Transfer Learning Model for Brain Tumors Classification

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

Contact Info

Product

Resources

About