Enhanced Brain Tumor Grade Classification with ConvNext: Performance Analysis

Clicks: 2
ID: 312740
2025
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #176 of 705 articles by views in Journal of Computing & Biomedical Informatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
The grade of a brain tumor is a crucial component of its diagnosis and aids in treatment planning. Biopsies and manual review of medical images are examples of traditional diagnostic techniques that are either invasive or may produce incorrect results. Using a contemporary convolutional neural network (CNN) architecture named ConvNext that receives magnetic resonance imaging (MRI) data, this study suggests a metho d for classifying brain tumor grades. In order to diagnose brain cancers consistently, accurately, and non-invasively, deep learning based metho ds are taking the place of invasive treatments. Data scarcity is a well-known issue with applying deep learning architectures to medical imaging. In order to attain the required accuracy and prevent overfitting, modern architectures need enormous datasets and contain enormous trainable parameters. As a result, researchers that use medical imaging data frequently use transfer learning. CNNs have recently lost ground to transformer-based designs for picture data. However, by implementing specific modifications influenced by vision transformers, freshly proposed CNNs have attained greater accuracy. A method for extracting features from the ConvNext architecture and feeding them into a fully connected neural network for final classification was presented in this article. Using pre-trained ConvNext, the proposed study obtained state-of-the-art performance on the BraTS 2019 dataset. When three MRI sequences were fed into the pre-trained CNN as three channels, the highest accuracy of 99.5% was attained.
Reference Key
imported_1777056397_69ebba8d6f442 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Faheem Mazhar
Journal Journal of Computing & Biomedical Informatics
Year 2025
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/1027
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.