Modified CNN Architecture for Efficient Classification of Glioma Brain Tumour

Jeba, Jenifer; Devi, S. Nirmala; Meena, M. Janaki

doi:10.1080/03772063.2022.2101553

Cited by 7 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their model achieved an F1-score of 96.50%, a precision of 96.50%, a recall of 96.49%, and an accuracy of 96.50%. This study [21] aims to categorize glioma tumors using a CNN, SVM, and k-nearest neighbours (KNN). The Cancer Imaging Archive database is used for research and experiments.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

A CNN-Model to Classify Low-Grade and High-Grade Glioma From MRI Images

et al. 2023

View full text Add to dashboard Cite

Glioma is the most occurring brain tumor in the world. Its grade (level of severity) identification, crucial in its treatment planning, is most demanding in a clinical environment. Computeraided methods have been experimented with to identify the grade of glioma, out of which deep learning-based methods, due to their auto features engineering, have a good impact in terms of their achieved outcomes. In this study, convolutional neural networks (CNNs) have been explored and utilized for the classification of glioma grading, for example, low grade (grade I-II) and high grade (grade III-IV). A CNN-based model, which is light-weighted in terms of layers, size, and learnable parameters, has been proposed. Experimental tests were carried out on benchmarked publicly available datasets, for example, Brats-2017, Brats-2018, & Brats-2019. A locally developed dataset from Bahawal Victoria Hospital, Bahawalpur, Pakistan, has also been employed for experimentation and research to cross-validate the outcomes. Additionally, experiments have been carried out to compare the effectiveness of the proposed model, and results have been compared with the results of state-of-the-art pertained CNN models, i.e., resnet18, squeezenet, and alexnet. The proposed model achieved maximum standard evaluation measures on the benchmarked dataset, i.e., accuracy, specificity, and sensitivity at 97.85%, 98.88%, and 99.88%, respectively. Similarly, these measures were 98.89%, 99.28%, and 99.77% on a locally developed dataset, which is the best compared to the recent stateof-the-art related studies.INDEX TERMS Low and high-grade glioma grading, convolutional neural networks, MRI images.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

“…The volume of the dataset is small. This study [21], classifies brain tumors using CNN, SVM, and KNN. The precision of the improved CNN is 94.60% and the accuracy of SVM and KNN is 86.1% and 66.7%, respectively.…”

Section: As Represented Inmentioning

confidence: 99%

A CNN-Model to Classify Low-Grade and High-Grade Glioma From MRI Images

et al. 2023

View full text Add to dashboard Cite

show abstract

“…In recent years, deep learning algorithms such as Convolutional Neural Networks (CNN) have made revolutionary breakthroughs in the analysis of brain glioma MR images, providing new opportunities for the automated detection and classification of brain tumor MRI data [9]. These algorithms can learn complex features from MRI data and perform remarkably well on classification and staging tasks [10][11][12]. However, there are also some limitations: (1) Due to the scarcity of MRI data and the imbalance of categories, MRI analysis in data-driven deep learning algorithms lacks the guidance of prior knowledge and domain-specific rules.…”

Section: Introductionmentioning

confidence: 99%

MCE: Medical Cognition Embedded in 3D MRI feature extraction for advancing glioma staging

Xue,

Lu,

Wang

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

In recent years, various data-driven algorithms have been applied to the classification and staging of brain glioma MRI detection. However, the restricted availability of brain glioma MRI data in purely data-driven deep learning algorithms has presented challenges in extracting high-quality features and capturing their complex patterns. Moreover, the analysis methods designed for 2D data necessitate the selection of ideal tumor image slices, which does not align with practical clinical scenarios. Our research proposes an novel brain glioma staging model, Medical Cognition Embedded (MCE) model for 3D data. This model embeds knowledge characteristics into data-driven approaches to enhance the quality of feature extraction. Approach includes the following key components: (1) Deep feature extraction, drawing upon the imaging technical characteristics of different MRI sequences, has led to the design of two methods at both the algorithmic and strategic levels to mimic the learning process of real image interpretation by medical professionals during film reading; (2) We conduct an extensive Radiomics feature extraction, capturing relevant features such as texture, morphology, and grayscale distribution; (3) By referencing key points in radiological diagnosis, Radiomics feature experimental results, and the imaging characteristics of various MRI sequences, we manually create diagnostic features (Diag-Features). The efficacy of proposed methodology is rigorously evaluated on the publicly available BraTS2018 and BraTS2020 datasets. Comparing it to most well-known purely data-driven models, our method achieved higher accuracy, recall, and precision, reaching 96.14%, 93.4%, 97.06%, and 97.57%, 92.80%, 95.96%, respectively.

show abstract

Harnessing Deep Learning for Accurate Pathological Assessment of Brain Tumor Cell Types

Tian,

Xi,

et al. 2024

J Digit Imaging. Inform. med.

View full text Add to dashboard Cite

Modified CNN Architecture for Efficient Classification of Glioma Brain Tumour

Cited by 7 publications

References 19 publications

A CNN-Model to Classify Low-Grade and High-Grade Glioma From MRI Images

A CNN-Model to Classify Low-Grade and High-Grade Glioma From MRI Images

MCE: Medical Cognition Embedded in 3D MRI feature extraction for advancing glioma staging

Harnessing Deep Learning for Accurate Pathological Assessment of Brain Tumor Cell Types

Contact Info

Product

Resources

About