脑肿瘤分析的进展:基于 MRI 分类和分割的机器学习、混合深度学习和迁移学习方法综述

IF 3 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Surajit Das, Rajat Subhra Goswami
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引用次数: 0

摘要

脑肿瘤,无论是癌症还是非癌症,都可能因细胞异常生长而危及生命,并可能导致器官功能障碍和成人死亡。脑肿瘤分割(BTS)和脑肿瘤分类(BTC)技术是诊断和治疗脑肿瘤的关键。它们有助于医生定位和测量肿瘤,并制定治疗和康复策略。尽管它们在医学领域非常重要,但脑肿瘤分类(BTC)和脑肿瘤分级(BTS)仍然充满挑战。这篇综述专门分析了机器学习和深度学习方法,包括卷积神经网络(CNN)、迁移学习(TL)以及用于 BTS 和 BTC 的混合模型。我们讨论了 U-Net++ 等卷积神经网络架构,该架构因其在二维和三维医学图像中的高分割准确性而闻名。此外,迁移学习利用来自 ImageNet 的 ResNet、Inception 等预训练模型,在脑肿瘤特定数据集上进行微调,以提高分类性能和灵敏度,尽管医疗数据有限。混合模型将深度学习技术与机器学习相结合,使用 CNN 进行初始分割并采用传统分类方法,从而提高了准确性。我们讨论了脑肿瘤研究中常用的基准数据集,包括 BraTS 数据集和 TCIA 数据库,并评估了 F1 分数、准确率、灵敏度、特异性和骰子系数等性能指标,强调了它们在脑肿瘤分析中的重要性和标准阈值。综述探讨了当前基于机器学习(ML)和深度学习(DL)的 BTS 和 BTC 挑战,并提出了可解释的深度学习模型和多任务学习框架等解决方案。这些见解旨在指导未来的进步,促进准确、高效工具的开发,改善脑肿瘤分析中的患者护理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Advancements in brain tumor analysis: a comprehensive review of machine learning, hybrid deep learning, and transfer learning approaches for MRI-based classification and segmentation

Advancements in brain tumor analysis: a comprehensive review of machine learning, hybrid deep learning, and transfer learning approaches for MRI-based classification and segmentation

Brain tumors, whether cancerous or noncancerous, can be life-threatening due to abnormal cell growth, potentially causing organ dysfunction and mortality in adults. Brain tumor segmentation (BTS) and brain tumor classification (BTC) technologies are crucial in diagnosing and treating brain tumors. They assist doctors in locating and measuring tumors and developing treatment and rehabilitation strategies. Despite their importance in the medical field, BTC and BTS remain challenging. This comprehensive review specifically analyses machine and deep learning methodologies, including convolutional neural networks (CNN), transfer learning (TL), and hybrid models for BTS and BTC. We discuss CNN architectures like U-Net++, which is known for its high segmentation accuracy in 2D and 3D medical images. Additionally, transfer learning utilises pre-trained models such as ResNet, Inception, etc., from ImageNet, fine-tuned on brain tumor-specific datasets to enhance classification performance and sensitivity despite limited medical data. Hybrid models combine deep learning techniques with machine learning, using CNN for initial segmentation and traditional classification methods, improving accuracy. We discuss commonly used benchmark datasets in brain tumors research, including the BraTS dataset and the TCIA database, and evaluate performance metrics, such as the F1-score, accuracy, sensitivity, specificity, and the dice coefficient, emphasising their significance and standard thresholds in brain tumors analysis. The review addresses current machine learning (ML) and deep learning (DL) based BTS and BTC challenges and proposes solutions such as explainable deep learning models and multi-task learning frameworks. These insights aim to guide future advancements in fostering the development of accurate and efficient tools for improved patient care in brain tumors analysis.

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来源期刊
Multimedia Tools and Applications
Multimedia Tools and Applications 工程技术-工程:电子与电气
CiteScore
7.20
自引率
16.70%
发文量
2439
审稿时长
9.2 months
期刊介绍: Multimedia Tools and Applications publishes original research articles on multimedia development and system support tools as well as case studies of multimedia applications. It also features experimental and survey articles. The journal is intended for academics, practitioners, scientists and engineers who are involved in multimedia system research, design and applications. All papers are peer reviewed. Specific areas of interest include: - Multimedia Tools: - Multimedia Applications: - Prototype multimedia systems and platforms
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