Deep CNNs for glioma grading on conventional MRIs: Performance analysis, challenges, and future directions.

IF 2.6 4区 工程技术 Q1 Mathematics
Sonam Saluja, Munesh Chandra Trivedi, Ashim Saha
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引用次数: 0

Abstract

The increasing global incidence of glioma tumors has raised significant healthcare concerns due to their high mortality rates. Traditionally, tumor diagnosis relies on visual analysis of medical imaging and invasive biopsies for precise grading. As an alternative, computer-assisted methods, particularly deep convolutional neural networks (DCNNs), have gained traction. This research paper explores the recent advancements in DCNNs for glioma grading using brain magnetic resonance images (MRIs) from 2015 to 2023. The study evaluated various DCNN architectures and their performance, revealing remarkable results with models such as hybrid and ensemble based DCNNs achieving accuracy levels of up to 98.91%. However, challenges persisted in the form of limited datasets, lack of external validation, and variations in grading formulations across diverse literature sources. Addressing these challenges through expanding datasets, conducting external validation, and standardizing grading formulations can enhance the performance and reliability of DCNNs in glioma grading, thereby advancing brain tumor classification and extending its applications to other neurological disorders.

用于传统磁共振成像胶质瘤分级的深度 CNN:性能分析、挑战和未来方向。
脑胶质瘤在全球的发病率不断上升,由于其死亡率高,引起了人们对医疗保健的极大关注。传统上,肿瘤诊断依赖于医学成像的视觉分析和侵入性活检来进行精确分级。作为一种替代方法,计算机辅助方法,尤其是深度卷积神经网络(DCNN),已获得广泛关注。本研究论文探讨了从2015年到2023年利用脑磁共振图像(MRI)进行胶质瘤分级的DCNNs的最新进展。研究评估了各种 DCNN 体系结构及其性能,结果显示,基于混合和集合的 DCNN 等模型的准确率高达 98.91%,成绩斐然。然而,由于数据集有限、缺乏外部验证以及不同文献来源的分级公式存在差异,挑战依然存在。通过扩大数据集、进行外部验证和标准化分级公式来应对这些挑战,可以提高 DCNN 在胶质瘤分级中的性能和可靠性,从而推进脑肿瘤分类并将其应用扩展到其他神经系统疾病。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Mathematical Biosciences and Engineering
Mathematical Biosciences and Engineering 工程技术-数学跨学科应用
CiteScore
3.90
自引率
7.70%
发文量
586
审稿时长
>12 weeks
期刊介绍: Mathematical Biosciences and Engineering (MBE) is an interdisciplinary Open Access journal promoting cutting-edge research, technology transfer and knowledge translation about complex data and information processing. MBE publishes Research articles (long and original research); Communications (short and novel research); Expository papers; Technology Transfer and Knowledge Translation reports (description of new technologies and products); Announcements and Industrial Progress and News (announcements and even advertisement, including major conferences).
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