Department of E & TC Engineering, College of Engineering, Pune, India.
Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark.
Curr Med Imaging. 2022;18(6):604-622. doi: 10.2174/1573405617666210923144739.
According to the International Agency for Research on Cancer (IARC), the mortality rate due to brain tumors is 76%. It is required to detect the brain tumors as early as possible and to provide the patient with the required treatment to avoid any fatal situation. With the recent advancement in technology, it is possible to automatically detect the tumor from images such as Magnetic Resonance Iimaging (MRI) and computed tomography scans using a computer-aided design. Machine learning and deep learning techniques have gained significance among researchers in medical fields, especially Convolutional Neural Networks (CNN), due to their ability to analyze large amounts of complex image data and perform classification. The objective of this review article is to present an exhaustive study of techniques such as preprocessing, machine learning, and deep learning that have been adopted in the last 15 years and based on it to present a detailed comparative analysis. The challenges encountered by researchers in the past for tumor detection have been discussed along with the future scopes that can be taken by the researchers as the future work. Clinical challenges that are encountered have also been discussed, which are missing in existing review articles.
根据国际癌症研究机构（IARC）的数据，脑瘤的死亡率为 76%。因此，需要尽早发现脑瘤，并为患者提供所需的治疗，以避免任何致命情况。随着技术的最新进展，可以使用计算机辅助设计，从磁共振成像（MRI）和计算机断层扫描等图像中自动检测肿瘤。机器学习和深度学习技术在医学领域的研究人员中引起了重视，尤其是卷积神经网络（CNN），因为它们能够分析大量复杂的图像数据并进行分类。本文的目的是对过去 15 年来采用的预处理、机器学习和深度学习等技术进行全面研究，并在此基础上进行详细的比较分析。讨论了过去研究人员在肿瘤检测中遇到的挑战，以及未来研究人员可以作为未来工作的方向。还讨论了临床挑战，这些挑战在现有的综述文章中是缺失的。
