Transfer Learning for Automatic Brain Tumor Classification Using MRI Images

Arbane, Mohamed; Benlamri, Rachid; Brik, Youcef; Djerioui, Mohamed

doi:10.1109/ihsh51661.2021.9378739

Cited by 66 publications

(13 citation statements)

References 13 publications

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“…However, the proposed work has used accuracy, precision, recall, F1 score, and AUC-ROC score to evaluate the performance of the various CNN architectures. To classify Brain Tumors, [17] implemented ResNet50 and Xception on 253 Brain MRIs. The performance of the model is evaluated using only the F1 score.…”

Section: Discussionmentioning

confidence: 99%

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Data Complexity Based Evaluation of the Model Dependence of Brain MRI Images for Classification of Brain Tumor and Alzheimer’s Disease

et al. 2022

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The convolutional neural networks (CNN) have shown promising results for various classification problems over the past years. However, selecting various CNN architectures is still challenging as each architecture performs differently with the same dataset. This research aims to evaluate the dependence of brain MRI on various predictive models of CNN based on the complexity of the data for Brain Tumor and Alzheimer's Disease. Our proposed approach has three parts. First part is the pre-processing of the data which mainly focuses on class balancing and the estimation of data complexity. The second part uses stratified k-fold cross-validation for more reliable results. The last part corresponds to the implementation of four CNN models applying described methods. This paper compares the classification performance of rigorous experimentation on four CNN variants namely S-CNN (CNN trained from scratch), ResNet50, InceptionV3, and Xception over two brain MRI image datasets evaluated with and without the use of Principal Component Analysis (PCA). The work benchmarks CNN models by comparing the average scores of Accuracy, Precision, Recall, F1 score, and AUC score from the stratified five-fold cross-validation.

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Section: Discussionmentioning

confidence: 99%

“…Similar works have been presented in [16] where apart from VGG-16, AlexNet, ResNet, InceptionV3, and DenseNet have been used. Arbane et al [17] proposed three CNN models, ResNet-50, Xception, and MobileNet-V2 for the detection of Brain Tumors on 253 brain MRI images and reported that MobileNet-V2 outperformed with an F1 score of 98.42%.…”

Section: Related Workmentioning

confidence: 99%

Data Complexity Based Evaluation of the Model Dependence of Brain MRI Images for Classification of Brain Tumor and Alzheimer’s Disease

et al. 2022

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“…A deep CNN was used in this study that based on transfer learning such as ResNet, Xception and Mobilenetv2 are utilized for the extraction of deep features has been for tumors classification using MRI images. This method achieved an accuracy of up to 98% [240]. In this method, Grab Cut method has been employed for segmentation of the brain lesions.…”

Section: Brain Tumor Detection Using Transfer Learningmentioning

confidence: 99%

Brain tumor detection and classification using machine learning: a comprehensive survey

Amin

Sharif

Haldorai³

et al. 2021

Complex Intell. Syst.

215

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Brain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial phase, it may lead to death. Despite many significant efforts and promising outcomes in this domain, accurate segmentation and classification remain a challenging task. A major challenge for brain tumor detection arises from the variations in tumor location, shape, and size. The objective of this survey is to deliver a comprehensive literature on brain tumor detection through magnetic resonance imaging to help the researchers. This survey covered the anatomy of brain tumors, publicly available datasets, enhancement techniques, segmentation, feature extraction, classification, and deep learning, transfer learning and quantum machine learning for brain tumors analysis. Finally, this survey provides all important literature for the detection of brain tumors with their advantages, limitations, developments, and future trends.

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“…Different techniques are developed for classifying and detecting brain tumors by utilizing MRI images. 7 The automatic identification using MRI is beneficial in various therapeutically and diagnostic applications. 8 The abnormality symptoms of the brain like injuries, and the effects are evaluated by the recognition of tumors and MRI is used for analyzing the abnormalities.…”

Section: Introductionmentioning

confidence: 99%

An optimal self adaptive deep neural network and spine‐kernelled chirplet transform for image registration

Pandi¹,

Senthilselvi

Maragatharajan

et al. 2022

Concurrency and Computation

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Image registration is one of the image processing techniques that align more than two images of a similar scene captured under different perspectives at different intervals of time. In demographical research, the image registration process assists to study differences in the structure of brain tissue. Due to enhanced technological advancements, numerous image registration methods have been established. On the other hand, these traditional techniques face few real-time challenges while processing huge input data. In addition to this, uncertainty analysis becomes a crucial step in medical applications which is utilized to judge whether the registration result is valuable or not.The high percentage of uncertainty than the threshold makes the registration result abnormal. Therefore, to conquer such circumstances, this research work proposed a modified spine-kernelled chirplet transform (MCST) based optimal Self-Adaptive Deep Neural Network (SADNN) which focuses mainly on enhancing registration accuracy by reducing the uncertainties of registration results. The experimental analysis is conducted and from the evaluation results, the proposed MCST-based optimal SADNN technique outperforms existing techniques in terms of accuracy, specificity, sensitivity, F-measure, and DICE values. Moreover, the proposed method achieves 97.2% accuracy for accurate image registration.

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Transfer Learning for Automatic Brain Tumor Classification Using MRI Images

Cited by 66 publications

References 13 publications

Data Complexity Based Evaluation of the Model Dependence of Brain MRI Images for Classification of Brain Tumor and Alzheimer’s Disease

Data Complexity Based Evaluation of the Model Dependence of Brain MRI Images for Classification of Brain Tumor and Alzheimer’s Disease

Brain tumor detection and classification using machine learning: a comprehensive survey

An optimal self adaptive deep neural network and spine‐kernelled chirplet transform for image registration

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