Brain Tumor Classification Based on Fine-Tuned Models and the Ensemble Method

Noreen, Neelum; Palaniappan, Sellappan; Qayyum, Abdul; Ahmad, Iftikhar; Alassafi, Madini O.

doi:10.32604/cmc.2021.014158

Cited by 54 publications

(52 citation statements)

References 19 publications

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“…If the Brain tumor is detected earlier, then actual situation of the tumor can be found and effective procedure can be taken to prevent the growth of the tumor. In this research, we discuss many pattern recognition algorithms for machine learning [1]. We have compared various machine learning & deep learning algorithms and based on that we finalize K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Convolutional Neural Network (CNN).…”

Section: Literature Reviewmentioning

confidence: 99%

“…In training phase, we have used the Convolutional Neural Network to extract feature maps from the image and this feature maps data is being forwarded to the selected Machine learning and Deep Learning algorithms. Traditional Machine Learning algorithms like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) classifier are fused on the basis of majority voting [1]. These algorithms need features maps produced from the last layer of model (shown in Fig.…”

Section: Training Modelsmentioning

confidence: 99%

“…11). Then uses their individual classification technique for tumor detection [1]. Nevertheless, theses classifiers requires a huge amount of training time to produce accurate prediction.…”

Section: Training Modelsmentioning

confidence: 99%

“…Performance Metrics 83%, and Random Forest (RF) is 77.87% from recent studies[1]. In our pre-trained model, we acquired accuracy level of 98.16% in EfficientNet-B3, 96.66% in Inception-ResNet-v2, 95.74% in ResNet-150v2, 88.07% in VGG16 and 85.46% in Temporal Convolutional Network (TCN)[26].…”

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confidence: 99%

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Multi-Modal Case Study on MRI Brain Tumor Detection Using Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbor, Temporal Convolution & Transfer Learning

Sutradhar

Tarefder

Prodan

et al. 2021

AJSE

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In the Medical field, Brain Tumor Detection has become a critical and demanding task because of its several shapes, locations, and intensity of image. That’s why an automated system is important to aid physicians and radiologists in detecting and classifying brain tumors. In this research, we have discussed different machine learning as well as deep learning algorithm which are mostly used for image classification. We have also compared different models that are being used for tumor classification based on machine learning and deep learning. MRI images of Glioma tumor, Pituitary tumor, Meningioma tumor are the base of this research, and we have compared different techniques along with the accuracy of different classification models using those MRI images. We have used different deep learning pre-trained models for training the brain tumor images. Those pre-trained models have provided outstanding performance along with less power consumption and computational time. EfficientNet-B3 has provided the best accuracy of 98.16% among other models as well as traditional machine learning algorithms. The experimental result of this model is proven the best and most efficient for tumor detection and classification in comparison with other recent studies.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Training Modelsmentioning

confidence: 99%

“…11). Then uses their individual classification technique for tumor detection [1]. Nevertheless, theses classifiers requires a huge amount of training time to produce accurate prediction.…”

Section: Training Modelsmentioning

confidence: 99%

mentioning

confidence: 99%

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Multi-Modal Case Study on MRI Brain Tumor Detection Using Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbor, Temporal Convolution & Transfer Learning

Sutradhar

Tarefder

Prodan

et al. 2021

AJSE

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“…Kumar et al [27] offered a weighted correlation feature selection-based iterative Bayesian multivariate deep neural learning (WCFS-IBMDNL) technique to improve early-stage brain tumor diagnosis. Noreen et al [28] presented a fine-tuned deep CNN model and encompassed the ensemble technique including RF, KNN, and SVM.…”

Section: Introductionmentioning

confidence: 99%

Synergy Factorized Bilinear Network with a Dual Suppression Strategy for Brain Tumor Classification in MRI

et al. 2021

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Automatic brain tumor classification is a practicable means of accelerating clinical diagnosis. Recently, deep convolutional neural network (CNN) training with MRI datasets has succeeded in computer-aided diagnostic (CAD) systems. To further improve the classification performance of CNNs, there is still a difficult path forward with regards to subtle discriminative details among brain tumors. We note that the existing methods heavily rely on data-driven convolutional models while overlooking what makes a class different from the others. Our study proposes to guide the network to find exact differences among similar tumor classes. We first present a “dual suppression encoding” block tailored to brain tumor MRIs, which diverges two paths from our network to refine global orderless information and local spatial representations. The aim is to use more valuable clues for correct classes by reducing the impact of negative global features and extending the attention of salient local parts. Then we introduce a “factorized bilinear encoding” layer for feature fusion. The aim is to generate compact and discriminative representations. Finally, the synergy between these two components forms a pipeline that learns in an end-to-end way. Extensive experiments exhibited superior classification performance in qualitative and quantitative evaluation on three datasets.

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Brain tumor classification by using a novel convolutional neural network structure

Polat

Dokur

Ölmez

2022

Int J Imaging Syst Tech

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Brain tumors located in the skull are among the health problems that cause serious consequences. Rapid and accurate detection of brain tumor types will ensure that the patient receives appropriate treatment in the early period, thus increasing the patient's chance of recovery and survival. In the literature, classification accuracies over 98% have been acquired automatically by using deep neural networks (DNN) for the brain tumor images such as glioma, meningioma, and pituitary. It is observed that researchers generally focused on achieving higher classification accuracy and therefore, they have used pre‐processing stages, augmentation processes, huge or hybrid DNN structures. These approaches have brought some disadvantages in terms of practical use of the developed methods: (i)The parameters of the pre‐processes should be carefully determined, otherwise the classification accuracy will decrease. (ii) In order to increase the classification performance, it is important to determine the coarse structure of the DNN correctly. If the DNN has many hyper‐parameters, the coarse structure will be determined in a long time. (iii) It is difficult to implement complex DNN structures or training algorithms in terms of practical use, because these methods need huge memory and high CPU computation. In this study, we have proposed a novel DNN model to increase the classification accuracy, and to decrease the number of weights in the structure, and to use less number of hyper‐parameters. We named this model, which uses a divergence‐based feature extractor, as DivFE‐v1 for short. 99.18% classification accuracy for the Figshare dataset is obtained by using the small‐sized DNN structure without any pre‐processing stage or augmentation process.

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Brain Tumor Classification Based on Fine-Tuned Models and the Ensemble Method

Cited by 54 publications

References 19 publications

Multi-Modal Case Study on MRI Brain Tumor Detection Using Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbor, Temporal Convolution & Transfer Learning

Multi-Modal Case Study on MRI Brain Tumor Detection Using Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbor, Temporal Convolution & Transfer Learning

Synergy Factorized Bilinear Network with a Dual Suppression Strategy for Brain Tumor Classification in MRI

Brain tumor classification by using a novel convolutional neural network structure

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