Brain Tumor Classification Using Fine-Tuned GoogLeNet Features and Machine Learning Algorithms: IoMT Enabled CAD System

Sekhar, Ardhendu; Biswas, Soumen; Hazra, Ranjay; Sunaniya, Arun Kumar; Mukherjee, Amrit; Yang, Lixia

doi:10.1109/jbhi.2021.3100758

Cited by 109 publications

(56 citation statements)

References 26 publications

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“…Manually designing robust features for ML algorithms can be very demanding and time-consuming. Therefore, some studies [ 13 , 14 , 84 ] used deep features to build brain tumor classification models. Deep features refer to features that are extracted from CNN models.…”

Section: Discussionmentioning

confidence: 99%

“…Results reported in the literature show that classical ML algorithms trained on deep features outperformed pre-trained models. For example, results reported by Sekhar et al [ 14 ] show that GoogleNet produced precision and specificity of 96.02% and 96.00%, respectively using the softmax classifier. The results also show that the performance of GoogleNet was improved by over 2.5% and 2.3% when SVM and k-NN classifiers were used.…”

Section: Discussionmentioning

confidence: 99%

“…For studies that used the same algorithm, we selected the algorithm that produced the best performance. As shown in Figure 11 , the SVM model designed by Sekhar [ 14 ] produced the best performance for ML-based tumor classification. In the study [ 14 ], SVM was trained on features extracted from the GoogleNet pre-trained model [ 14 ].…”

Section: Discussionmentioning

confidence: 99%

“…Sekhar et al [ 14 ] proposed a tumor classification model using a modified GoogleNet pre-trained CNN model [ 83 ] and two ML algorithms: SVM and k-NN. In the study, the last three fully connected layers of GoogleNet network were modified and fine-tuned on brain tumor images.…”

Section: Literature Surveymentioning

confidence: 99%

“…Computer-aided diagnosis has proven to be useful in supporting medical practitioners [ 12 ]. It can be performed using different techniques including classical machine learning (ML) [ 13 , 14 ] (such as clustering [ 15 ]), Convolutional Neural Network (CNN) approaches [ 16 , 17 ], recently considering also capsule neural networks (CapsNets) [ 18 , 19 ], and Vision Transformers (ViTs) [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

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Brain Tumor Diagnosis Using Machine Learning, Convolutional Neural Networks, Capsule Neural Networks and Vision Transformers, Applied to MRI: A Survey

et al. 2022

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Management of brain tumors is based on clinical and radiological information with presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of paramount importance to choose the best treatment plan. Convolutional Neural Networks (CNNs) represent one of the effective Deep Learning (DL)-based techniques that have been used for brain tumor diagnosis. However, they are unable to handle input modifications effectively. Capsule neural networks (CapsNets) are a novel type of machine learning (ML) architecture that was recently developed to address the drawbacks of CNNs. CapsNets are resistant to rotations and affine translations, which is beneficial when processing medical imaging datasets. Moreover, Vision Transformers (ViT)-based solutions have been very recently proposed to address the issue of long-range dependency in CNNs. This survey provides a comprehensive overview of brain tumor classification and segmentation techniques, with a focus on ML-based, CNN-based, CapsNet-based, and ViT-based techniques. The survey highlights the fundamental contributions of recent studies and the performance of state-of-the-art techniques. Moreover, we present an in-depth discussion of crucial issues and open challenges. We also identify some key limitations and promising future research directions. We envisage that this survey shall serve as a good springboard for further study.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Brain Tumor Diagnosis Using Machine Learning, Convolutional Neural Networks, Capsule Neural Networks and Vision Transformers, Applied to MRI: A Survey

et al. 2022

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MRI radiomics model for predicting TERT promoter mutation status in glioblastoma

Chen,

et al. 2023

Brain and Behavior

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Background and purposeThe presence of TERT promoter mutations has been associated with worse prognosis and resistance to therapy for patients with glioblastoma (GBM). This study aimed to determine whether the combination model of different feature selections and classification algorithms based on multiparameter MRI can be used to predict TERT subtype in GBM patients.MethodsA total of 143 patients were included in our retrospective study, and 2553 features were obtained. The datasets were randomly divided into training and test sets in a ratio of 7:3. The synthetic minority oversampling technique was used to achieve data balance. The Pearson correlation coefficients were used for dimension reduction. Three feature selections and five classification algorithms were used to model the selected features. Finally, 10‐fold cross validation was applied to the training dataset.ResultsA model with eight features generated by recursive feature elimination (RFE) and linear discriminant analysis (LDA) showed the greatest diagnostic performance (area under the curve values for the training, validation, and testing sets: 0.983, 0.964, and 0.926, respectively), followed by relief and random forest (RF), analysis of variance and RF. Furthermore, the relief was the optimal feature selection for separately evaluating those five classification algorithms, and RF was the most preferable algorithm for separately assessing the three feature selectors. ADC entropy was the parameter that made the greatest contribution to the discrimination of TERT mutations.ConclusionsRadiomics model generated by RFE and LDA mainly based on ADC entropy showed good performance in predicting TERT promoter mutations in GBM.

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Potential roles of transformers in brain tumor diagnosis and treatment

Lan

Zou

Qin

et al. 2023

Brain-X

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Brain tumor (BT) is one of many malignancies that have substantially enhanced global human morbidity and mortality rates. Early detection and characterization of glioma are essential for effective preventive strategies. Currently, the use of Transformers, a deep learning model for BT diagnosis and treatment, is attracting significant attention. The transformer self‐attention mechanism automatically learns the associations between input data for efficient processing and analysis. Research indicates that Transformers could play an essential role in the BT segmentation of magnetic resonance imaging (MRI) images, the MRI and histopathology‐based grading of brain cancer, BT molecular expression prediction, the classification of primary brain metastasis sites, voxel‐level dose and BT radiotherapy outcome prediction, synergistic prediction, and the pathway deconvolution of drug combinations. In this review, the feasibility, accuracy, and applicability of various algorithms are systematically analyzed and their prospects are discussed. Overall, this review aimed to discuss and provide an overview of the increasing applications of Transformers in real‐time BT detection and therapy, indicating their broad prospects and potential. In the future, Transformers are expected to be increasingly used for the diagnosis and subsequent treatment of BT because of the continuous development and improvement of Transformer‐based deep learning technology. However, more work is required to investigate their properties for anomaly detection, medical image classification, network design development, and application to other medical data.

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Brain Tumor Classification Using Fine-Tuned GoogLeNet Features and Machine Learning Algorithms: IoMT Enabled CAD System

Cited by 109 publications

References 26 publications

Brain Tumor Diagnosis Using Machine Learning, Convolutional Neural Networks, Capsule Neural Networks and Vision Transformers, Applied to MRI: A Survey

Brain Tumor Diagnosis Using Machine Learning, Convolutional Neural Networks, Capsule Neural Networks and Vision Transformers, Applied to MRI: A Survey

MRI radiomics model for predicting TERT promoter mutation status in glioblastoma

Potential roles of transformers in brain tumor diagnosis and treatment

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