由于多种致命疾病，数百万人失去生命。癌症是其中一种最致命的疾病，可能与肥胖、酒精摄入、感染、紫外线辐射、吸烟和不健康的生活方式有关。癌症是机体内异常和不受控制的组织生长，可能会扩散到非原发部位的其他人体部位。因此，在早期诊断癌症以提供正确和及时的治疗非常重要。此外，手动诊断和诊断错误可能导致许多患者死亡，因此正在对癌症早期自动和准确检测进行大量研究。 本文我们对使用传统机器学习（ML）和深度学习（DL）模型进行各种癌症类型的诊断和最新进展进行了比较分析。在这项研究中，我们包括了四种癌症类型，即脑、肺、皮肤和乳腺，并使用ML和DL技术进行了它们的检测。在广泛的文献回顾中，我们包括了共130篇文献，其中56篇基于ML，74篇基于DL的癌症检测技术。仅打算分析近五年（2018年至2023年）发表的经过同行评议的研究论文，根据参数、出版年份、所使用的特征、最佳模型、数据集/图像使用情况以及最佳准确性进行分析。我们分别评估了基于ML和DL的癌症检测技术，并将准确性作为性能评估指标，以维持分类器效率的同质性。 在所有回顾的文献中，DL技术的准确性最高，达到了100%，而ML技术的准确性为99.89%。DL和ML方法的最低准确性分别为70%和75.48%。在皮肤癌检测中，最高和最低性能模型之间的准确性差异约为28.8%。此外，还提出了使用ML和DL技术进行每种癌症检测的关键发现和挑战。为了未来的研究目的，提供了最佳性能模型和最差性能模型之间的比较分析，同时呈现了总体关键发现和挑战。尽管该分析基于准确性作为性能指标和各种参数，但结果显示在分类效率方面有很大的改进空间。 本文得出结论，ML和DL技术在各种癌症类型的早期检测方面具有前景。然而，该研究确定了需要解决的具体挑战，以便在临床环境中广泛应用这些技术。所呈现的结果为癌症检测的未来研究提供了有价值的指导，强调了在ML和DL技术应用方面持续进步的必要性，以提高诊断准确性并最终拯救更多生命。 © 2023年该作者/作者在Springer Nature下的独家许可下，授权给Springer-Verlag GmbH Germany部分所有。

There are millions of people who lose their life due to several types of fatal diseases. Cancer is one of the most fatal diseases which may be due to obesity, alcohol consumption, infections, ultraviolet radiation, smoking, and unhealthy lifestyles. Cancer is abnormal and uncontrolled tissue growth inside the body which may be spread to other body parts other than where it has originated. Hence it is very much required to diagnose the cancer at an early stage to provide correct and timely treatment. Also, manual diagnosis and diagnostic error may cause of the death of many patients hence much research are going on for the automatic and accurate detection of cancer at early stage.In this paper, we have done the comparative analysis of the diagnosis and recent advancement for the detection of various cancer types using traditional machine learning (ML) and deep learning (DL) models. In this study, we have included four types of cancers, brain, lung, skin, and breast and their detection using ML and DL techniques. In extensive review we have included a total of 130 pieces of literature among which 56 are of ML-based and 74 are from DL-based cancer detection techniques. Only the peer reviewed research papers published in the recent 5-year span (2018-2023) have been included for the analysis based on the parameters, year of publication, feature utilized, best model, dataset/images utilized, and best accuracy. We have reviewed ML and DL-based techniques for cancer detection separately and included accuracy as the performance evaluation metrics to maintain the homogeneity while verifying the classifier efficiency.Among all the reviewed literatures, DL techniques achieved the highest accuracy of 100%, while ML techniques achieved 99.89%. The lowest accuracy achieved using DL and ML approaches were 70% and 75.48%, respectively. The difference in accuracy between the highest and lowest performing models is about 28.8% for skin cancer detection. In addition, the key findings, and challenges for each type of cancer detection using ML and DL techniques have been presented. The comparative analysis between the best performing and worst performing models, along with overall key findings and challenges, has been provided for future research purposes. Although the analysis is based on accuracy as the performance metric and various parameters, the results demonstrate a significant scope for improvement in classification efficiency.The paper concludes that both ML and DL techniques hold promise in the early detection of various cancer types. However, the study identifies specific challenges that need to be addressed for the widespread implementation of these techniques in clinical settings. The presented results offer valuable guidance for future research in cancer detection, emphasizing the need for continued advancements in ML and DL-based approaches to improve diagnostic accuracy and ultimately save more lives.© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.