An early detection and segmentation of Brain Tumor using Deep Neural Network

Aggarwal, Mukul; Tiwari, Abhishek; Mangipudi, Parthasarathi; Bijalwan, Anchit

doi:10.1186/s12911-023-02174-8

Cited by 39 publications

(8 citation statements)

References 37 publications

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“…In addition, multiple radiologists annotating images in a dataset may result in discrepancies (i.e., inter- and intra-reader variability), causing the deep learning models to underperform. 22 Furthermore, labelling images places an additional burden on radiologists. It is anticipated that the automated tumour segmentation and classification algorithms will be capable of providing robust and consistent results while alleviating the workload of radiologists.…”

Section: Discussionmentioning

confidence: 99%

MRI-Based End-To-End Pediatric Low-Grade Glioma Segmentation and Classification

et al. 2023

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Purpose: MRI-based radiomics models can predict genetic markers in pediatric low-grade glioma (pLGG). These models usually require tumour segmentation, which is tedious and time consuming if done manually. We propose a deep learning (DL) model to automate tumour segmentation and build an end-to-end radiomics-based pipeline for pLGG classification. Methods: The proposed architecture is a 2-step U-Net based DL network. The first U-Net is trained on downsampled images to locate the tumour. The second U-Net is trained using image patches centred around the located tumour to produce more refined segmentations. The segmented tumour is then fed into a radiomics-based model to predict the genetic marker of the tumour. Results: Our segmentation model achieved a correlation value of over 80% for all volume-related radiomic features and an average Dice score of .795 in test cases. Feeding the auto-segmentation results into a radiomics model resulted in a mean area under the ROC curve (AUC) of .843, with 95% confidence interval (CI) [.78-.906] and .730, with 95% CI [.671-.789] on the test set for 2-class (BRAF V600E mutation BRAF fusion) and 3-class (BRAF V600E mutation BRAF fusion and Other) classification, respectively. This result was comparable to the AUC of .874, 95% CI [.829-.919] and .758, 95% CI [.724-.792] for the radiomics model trained and tested on the manual segmentations in 2-class and 3-class classification scenarios, respectively. Conclusion: The proposed end-to-end pipeline for pLGG segmentation and classification produced results comparable to manual segmentation when it was used for a radiomics-based genetic marker prediction model.

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Section: Discussionmentioning

confidence: 99%

MRI-Based End-To-End Pediatric Low-Grade Glioma Segmentation and Classification

et al. 2023

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“…With this, it is possible to propagate the information better and avoid the fading of the gradient in the backpropagation phase. Numerous recent studies have been conducted in the field of tumor detection utilizing ResNet, showcasing the remarkable performance and efficacy of this architectural approach ( El-Feshawy et al, 2023 ; Shehab et al, 2021 ; Aggarwal et al, 2023 ).…”

Section: Methodsmentioning

confidence: 99%

Survival and grade of the glioma prediction using transfer learning

Valbuena Rubio,

García-Ordás,

García-Olalla Olivera

et al. 2023

PeerJ Computer Science

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Glioblastoma is a highly malignant brain tumor with a life expectancy of only 3–6 months without treatment. Detecting and predicting its survival and grade accurately are crucial. This study introduces a novel approach using transfer learning techniques. Various pre-trained networks, including EfficientNet, ResNet, VGG16, and Inception, were tested through exhaustive optimization to identify the most suitable architecture. Transfer learning was applied to fine-tune these models on a glioblastoma image dataset, aiming to achieve two objectives: survival and tumor grade prediction.The experimental results show 65% accuracy in survival prediction, classifying patients into short, medium, or long survival categories. Additionally, the prediction of tumor grade achieved an accuracy of 97%, accurately differentiating low-grade gliomas (LGG) and high-grade gliomas (HGG). The success of the approach is attributed to the effectiveness of transfer learning, surpassing the current state-of-the-art methods. In conclusion, this study presents a promising method for predicting the survival and grade of glioblastoma. Transfer learning demonstrates its potential in enhancing prediction models, particularly in scenarios with limited large datasets. These findings hold promise for improving diagnostic and treatment approaches for glioblastoma patients.

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“…An automated method utilizing morphological-based segmentation was proposed for precise tumor detection in MRI images ( Albalawi et al, 2024 ). Deep learning techniques, specifically a 2D CNN, were employed for early detection of various brain tumors ( Mahesh et al, 2024 ), while an Improved Residual Network (ResNet) aimed to enhance segmentation accuracy ( Aggarwal et al, 2023 ). An FPGA-based accelerator was introduced to improve segmentation speed and accuracy ( Xiong et al, 2021 ), and a YOLO2-based transfer learning approach achieved high classification accuracy ( Kumar Sahoo et al, 2023 ).…”

Section: Related Workmentioning

confidence: 99%

Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach

Albalawi,

Thakur,

Dorai

et al. 2024

Front. Comput. Neurosci.

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BackgroundThe necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies and patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretation, grapples with challenges such as time-intensive processes and susceptibility to human error.ObjectiveThis research presents a novel Convolutional Neural Network (CNN) architecture designed to enhance the accuracy and efficiency of brain tumor detection in MRI scans.MethodsThe dataset used in the study comprises 7,023 brain MRI images from figshare, SARTAJ, and Br35H, categorized into glioma, meningioma, no tumor, and pituitary classes, with a CNN-based multi-task classification model employed for tumor detection, classification, and location identification. Our methodology focused on multi-task classification using a single CNN model for various brain MRI classification tasks, including tumor detection, classification based on grade and type, and tumor location identification.ResultsThe proposed CNN model incorporates advanced feature extraction capabilities and deep learning optimization techniques, culminating in a groundbreaking paradigm shift in automated brain MRI analysis. With an exceptional tumor classification accuracy of 99%, our method surpasses current methodologies, demonstrating the remarkable potential of deep learning in medical applications.ConclusionThis study represents a significant advancement in the early detection and treatment planning of brain tumors, offering a more efficient and accurate alternative to traditional MRI analysis methods.

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An early detection and segmentation of Brain Tumor using Deep Neural Network

Cited by 39 publications

References 37 publications

MRI-Based End-To-End Pediatric Low-Grade Glioma Segmentation and Classification

MRI-Based End-To-End Pediatric Low-Grade Glioma Segmentation and Classification

Survival and grade of the glioma prediction using transfer learning

Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach

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