Kaifi Reham
Department of Radiological Sciences, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Jeddah City 22384, Saudi Arabia.
King Abdullah International Medical Research Center, Jeddah City 22384, Saudi Arabia.
Diagnostics (Basel). 2023 Sep 20;13(18):3007. doi: 10.3390/diagnostics13183007.
Uncontrolled and fast cell proliferation is the cause of brain tumors. Early cancer detection is vitally important to save many lives. Brain tumors can be divided into several categories depending on the kind, place of origin, pace of development, and stage of progression; as a result, tumor classification is crucial for targeted therapy. Brain tumor segmentation aims to delineate accurately the areas of brain tumors. A specialist with a thorough understanding of brain illnesses is needed to manually identify the proper type of brain tumor. Additionally, processing many images takes time and is tiresome. Therefore, automatic segmentation and classification techniques are required to speed up and enhance the diagnosis of brain tumors. Tumors can be quickly and safely detected by brain scans using imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and others. Machine learning (ML) and artificial intelligence (AI) have shown promise in developing algorithms that aid in automatic classification and segmentation utilizing various imaging modalities. The right segmentation method must be used to precisely classify patients with brain tumors to enhance diagnosis and treatment. This review describes multiple types of brain tumors, publicly accessible datasets, enhancement methods, segmentation, feature extraction, classification, machine learning techniques, deep learning, and learning through a transfer to study brain tumors. In this study, we attempted to synthesize brain cancer imaging modalities with automatically computer-assisted methodologies for brain cancer characterization in ML and DL frameworks. Finding the current problems with the engineering methodologies currently in use and predicting a future paradigm are other goals of this article.
不受控制的快速细胞增殖是脑肿瘤的病因。早期癌症检测对于挽救许多生命至关重要。脑肿瘤可根据种类、起源部位、发展速度和进展阶段分为几类；因此，肿瘤分类对于靶向治疗至关重要。脑肿瘤分割旨在准确勾勒出脑肿瘤区域。需要一位对脑部疾病有透彻了解的专家来手动识别脑肿瘤的正确类型。此外，处理大量图像既耗时又繁琐。因此，需要自动分割和分类技术来加快和加强脑肿瘤的诊断。使用包括计算机断层扫描（CT）、磁共振成像（MRI）等在内的成像模态，通过脑部扫描可以快速、安全地检测肿瘤。机器学习（ML）和人工智能（AI）在开发利用各种成像模态辅助自动分类和分割的算法方面已显示出前景。必须使用正确的分割方法来精确分类脑肿瘤患者，以加强诊断和治疗。这篇综述描述了多种类型的脑肿瘤、公开可用的数据集、增强方法、分割、特征提取、分类、机器学习技术、深度学习以及通过迁移学习来研究脑肿瘤。在本研究中，我们试图在ML和DL框架中，将脑癌成像模态与用于脑癌特征描述的自动计算机辅助方法相结合。找出当前使用的工程方法存在的问题并预测未来的范式也是本文的其他目标。
