癌症的特点是异常细胞不受控制地分裂，损害身体组织，因此需要早期发现才能有效治疗。医学成像对于识别各种癌症至关重要，但放射科医生的手动解释通常是主观的、劳动密集型且耗时的。因此，迫切需要自动化决策过程来增强癌症检测和诊断。此前，人们对不同的癌症检测方法进行了大量的调查研究，但大多数都集中在特定的癌症和有限的技术上。这项研究对癌症检测方法进行了全面的调查。它回顾了从 Web of Science、IEEE 和 Scopus 数据库收集的 99 篇研究文章，这些文章发表于 2020 年至 2024 年之间。研究范围涵盖 12 种类型的癌症，包括乳腺癌、宫颈癌、卵巢癌、前列腺癌、食管癌、肝癌、胰腺癌、结肠癌、肺癌、口腔癌、脑癌和皮肤癌。本研究讨论了不同的癌症检测技术，包括医学成像数据、图像预处理、分割、特征提取、深度学习和迁移学习方法以及评估指标。最终，我们总结了数据集和技术以及研究挑战和局限性。最后，我们提供了增强癌症检测技术的未来方向。版权所有 © 2024 作者。由 Elsevier B.V. 出版。保留所有权利。

Cancer, characterized by the uncontrolled division of abnormal cells that harm body tissues, necessitates early detection for effective treatment. Medical imaging is crucial for identifying various cancers, yet its manual interpretation by radiologists is often subjective, labour-intensive, and time-consuming. Consequently, there is a critical need for an automated decision-making process to enhance cancer detection and diagnosis. Previously, a lot of work was done on surveys of different cancer detection methods, and most of them were focused on specific cancers and limited techniques. This study presents a comprehensive survey of cancer detection methods. It entails a review of 99 research articles collected from the Web of Science, IEEE, and Scopus databases, published between 2020 and 2024. The scope of the study encompasses 12 types of cancer, including breast, cervical, ovarian, prostate, esophageal, liver, pancreatic, colon, lung, oral, brain, and skin cancers. This study discusses different cancer detection techniques, including medical imaging data, image preprocessing, segmentation, feature extraction, deep learning and transfer learning methods, and evaluation metrics. Eventually, we summarised the datasets and techniques with research challenges and limitations. Finally, we provide future directions for enhancing cancer detection techniques.Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.