Bayesian Depth-Wise Convolutional Neural Network Design for Brain Tumor MRI Classification

Ekong, Favour; Yu, Yongbin; Patamia, Rutherford Agbeshi; Xing, Feng; Tang, Qian; Mazumder, Pinaki; Cai, Jingye

doi:10.3390/diagnostics12071657

Cited by 17 publications

(7 citation statements)

References 31 publications

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“…Ekong et al [ 41 ] introduced a Bayesian-CNN approach, and while Bayesian methods offer probabilistic insights, they might not always capture the intricate features of brain tumors. While the GAN-Softmax approach by Asiri et al’s [ 42 ] model offers certain advancements, it is computationally more demanding.…”

Section: Discussionmentioning

confidence: 99%

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Brain Tumor Classification from MRI Using Image Enhancement and Convolutional Neural Network Techniques

Rasheed,

Ma,

Ullah

et al. 2023

Brain Sciences

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The independent detection and classification of brain malignancies using magnetic resonance imaging (MRI) can present challenges and the potential for error due to the intricate nature and time-consuming process involved. The complexity of the brain tumor identification process primarily stems from the need for a comprehensive evaluation spanning multiple modules. The advancement of deep learning (DL) has facilitated the emergence of automated medical image processing and diagnostics solutions, thereby offering a potential resolution to this issue. Convolutional neural networks (CNNs) represent a prominent methodology in visual learning and image categorization. The present study introduces a novel methodology integrating image enhancement techniques, specifically, Gaussian-blur-based sharpening and Adaptive Histogram Equalization using CLAHE, with the proposed model. This approach aims to effectively classify different categories of brain tumors, including glioma, meningioma, and pituitary tumor, as well as cases without tumors. The algorithm underwent comprehensive testing using benchmarked data from the published literature, and the results were compared with pre-trained models, including VGG16, ResNet50, VGG19, InceptionV3, and MobileNetV2. The experimental findings of the proposed method demonstrated a noteworthy classification accuracy of 97.84%, a precision success rate of 97.85%, a recall rate of 97.85%, and an F1-score of 97.90%. The results presented in this study showcase the exceptional accuracy of the proposed methodology in accurately classifying the most commonly occurring brain tumor types. The technique exhibited commendable generalization properties, rendering it a valuable asset in medicine for aiding physicians in making precise and proficient brain diagnoses.

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Section: Discussionmentioning

confidence: 99%

“…Ekong et al integrated depth-wise separable convolutions with Bayesian techniques to precisely classify and predict brain cancers. The recommended technique demonstrated superior performance compared to existing methods in terms of an accuracy of 94.32% [ 41 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Brain Tumor Classification from MRI Using Image Enhancement and Convolutional Neural Network Techniques

Rasheed,

Ma,

Ullah

et al. 2023

Brain Sciences

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“…In addition to the traditional radiomics and convolutional neural networks (CNN) approaches [ 17 , 18 ], an algorithm combining radiomics with graph convolutional networks (GCN) architecture (Radio-GCN) was designed for the prediction of EGFR genomic status based on brain MRI from NSCLC patients with BM. Traditional approaches were applied as previously described [ 20 , 21 ]. As for Radio-GCN (Fig.…”

Section: Methodsmentioning

confidence: 99%

A deep learning model integrating multisequence MRI to predict EGFR mutation subtype in brain metastases from non-small cell lung cancer

Li,

Lv,

Chen

et al. 2024

Eur Radiol Exp

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Background To establish a predictive model based on multisequence magnetic resonance imaging (MRI) using deep learning to identify wild-type (WT) epidermal growth factor receptor (EGFR), EGFR exon 19 deletion (19Del), and EGFR exon 21-point mutation (21L858R) simultaneously. Methods A total of 399 patients with proven brain metastases of non-small cell lung cancer (NSCLC) were retrospectively enrolled and divided into training (n = 306) and testing (n = 93) cohorts separately based on two timepoints. All patients underwent 3.0-T brain MRI including T2-weighted, T2-weighted fluid-attenuated inversion recovery, diffusion-weighted imaging, and contrast-enhanced T1-weighted sequences. Radiomics features were extracted from each lesion based on four sequences. An algorithm combining radiomics approach with graph convolutional networks architecture (Radio-GCN) was designed for the prediction of EGFR mutation status and subtype. The area under the curve (AUC) at receiver operating characteristic analysis was used to evaluate the predication capabilities of each model. Results We extracted 1,290 radiomics features from each MRI sequence. The AUCs of the Radio-GCN model for identifying EGFR 19Del, 21L858R, and WT for the lesion-wise analysis were 0.996 ± 0.004, 0.971 ± 0.013, and 1.000 ± 0.000 on the independent testing cohort separately. It also yielded AUCs of 1.000 ± 0.000, 0.991 ± 0.009, and 1.000 ± 0.000 for predicting EGFR mutations respectively for the patient-wise analysis. The κ coefficients were 0.735 and 0.812, respectively. Conclusions The constructed Radio-GCN model is a new potential tool to predict the EGFR mutation status and subtype in NSCLC patients with brain metastases. Relevance statement The study demonstrated that a deep learning approach based on multisequence MRI can help to predict the EGFR mutation status in NSCLC patients with brain metastases, which is beneficial to guide a personalized treatment. Key points • This is the first study to predict the EGFR mutation subtype simultaneously. • The Radio-GCN model holds the potential to be used as a diagnostic tool. • This study provides an imaging surrogate for identifying the EGFR mutation subtype. Graphical Abstract

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“…MRI is a non-invasive medical imaging method that creates images of inside body components. This method creates detailed images of the body’s organs, tissues, and bones using a computer, radio waves, and a strong magnetic field [ 23 ]. According to the recent literature [ 24 ], MRI has been the most popular diagnostic technique for detecting AD which is shown in Figure 2 due the distribution of usage of various imaging modalities.…”

Section: Overview Of Deep Learning and Lstm Modelmentioning

confidence: 99%

An Approach to Binary Classification of Alzheimer’s Disease Using LSTM

et al. 2023

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In this study, we use LSTM (Long-Short-Term-Memory) networks to evaluate Magnetic Resonance Imaging (MRI) data to overcome the shortcomings of conventional Alzheimer’s disease (AD) detection techniques. Our method offers greater reliability and accuracy in predicting the possibility of AD, in contrast to cognitive testing and brain structure analyses. We used an MRI dataset that we downloaded from the Kaggle source to train our LSTM network. Utilizing the temporal memory characteristics of LSTMs, the network was created to efficiently capture and evaluate the sequential patterns inherent in MRI scans. Our model scored a remarkable AUC of 0.97 and an accuracy of 98.62%. During the training process, we used Stratified Shuffle-Split Cross Validation to make sure that our findings were reliable and generalizable. Our study adds significantly to the body of knowledge by demonstrating the potential of LSTM networks in the specific field of AD prediction and extending the variety of methods investigated for image classification in AD research. We have also designed a user-friendly Web-based application to help with the accessibility of our developed model, bridging the gap between research and actual deployment.

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Bayesian Depth-Wise Convolutional Neural Network Design for Brain Tumor MRI Classification

Cited by 17 publications

References 31 publications

Brain Tumor Classification from MRI Using Image Enhancement and Convolutional Neural Network Techniques

Brain Tumor Classification from MRI Using Image Enhancement and Convolutional Neural Network Techniques

A deep learning model integrating multisequence MRI to predict EGFR mutation subtype in brain metastases from non-small cell lung cancer

An Approach to Binary Classification of Alzheimer’s Disease Using LSTM

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