Berghout Tarek
Laboratory of Automation and Manufacturing Engineering, Department of Industrial Engineering, Batna 2 University, Batna 05000, Algeria.
J Imaging. 2024 Dec 24;11(1):2. doi: 10.3390/jimaging11010002.
Brain tumor detection is crucial in medical research due to high mortality rates and treatment challenges. Early and accurate diagnosis is vital for improving patient outcomes, however, traditional methods, such as manual Magnetic Resonance Imaging (MRI) analysis, are often time-consuming and error-prone. The rise of deep learning has led to advanced models for automated brain tumor feature extraction, segmentation, and classification. Despite these advancements, comprehensive reviews synthesizing recent findings remain scarce. By analyzing over 100 research papers over past half-decade (2019-2024), this review fills that gap, exploring the latest methods and paradigms, summarizing key concepts, challenges, datasets, and offering insights into future directions for brain tumor detection using deep learning. This review also incorporates an analysis of previous reviews and targets three main aspects: feature extraction, segmentation, and classification. The results revealed that research primarily focuses on Convolutional Neural Networks (CNNs) and their variants, with a strong emphasis on transfer learning using pre-trained models. Other methods, such as Generative Adversarial Networks (GANs) and Autoencoders, are used for feature extraction, while Recurrent Neural Networks (RNNs) are employed for time-sequence modeling. Some models integrate with Internet of Things (IoT) frameworks or federated learning for real-time diagnostics and privacy, often paired with optimization algorithms. However, the adoption of eXplainable AI (XAI) remains limited, despite its importance in building trust in medical diagnostics. Finally, this review outlines future opportunities, focusing on image quality, underexplored deep learning techniques, expanding datasets, and exploring deeper learning representations and model behavior such as recurrent expansion to advance medical imaging diagnostics.
由于高死亡率和治疗挑战，脑肿瘤检测在医学研究中至关重要。早期准确的诊断对于改善患者预后至关重要，然而，传统方法，如手动磁共振成像（MRI）分析，往往耗时且容易出错。深度学习的兴起催生了用于自动脑肿瘤特征提取、分割和分类的先进模型。尽管有这些进展，但综合近期研究结果的全面综述仍然很少。通过分析过去五年（2019 - 2024年）的100多篇研究论文，本综述填补了这一空白，探索了最新方法和范式，总结了关键概念、挑战、数据集，并对使用深度学习进行脑肿瘤检测的未来方向提供了见解。本综述还纳入了对先前综述的分析，并针对三个主要方面：特征提取、分割和分类。结果表明，研究主要集中在卷积神经网络（CNN）及其变体上，特别强调使用预训练模型的迁移学习。其他方法，如生成对抗网络（GAN）和自动编码器，用于特征提取，而循环神经网络（RNN）用于时间序列建模。一些模型与物联网（IoT）框架或联邦学习集成，以实现实时诊断和隐私保护，通常与优化算法相结合。然而，可解释人工智能（XAI）的应用仍然有限，尽管它在建立对医学诊断的信任方面很重要。最后，本综述概述了未来的机会，重点关注图像质量、未充分探索的深度学习技术、扩展数据集，以及探索更深层次的学习表示和模型行为，如循环扩展，以推进医学成像诊断。
