Rai Hari Mohan, Yoo Joon
School of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, 13120, Gyeonggi-do, Republic of Korea.
J Cancer Res Clin Oncol. 2023 Nov;149(15):14365-14408. doi: 10.1007/s00432-023-05216-w. Epub 2023 Aug 4.
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.
数以百万计的人因多种致命疾病而失去生命。癌症是最致命的疾病之一，可能由肥胖、饮酒、感染、紫外线辐射、吸烟和不健康的生活方式引起。癌症是体内异常且不受控制的组织生长，可能会扩散到其起源部位以外的其他身体部位。因此，非常需要在早期阶段诊断癌症，以便提供正确及时的治疗。此外，人工诊断和诊断错误可能导致许多患者死亡，因此正在进行大量研究，以实现癌症的早期自动准确检测。
在本文中，我们对使用传统机器学习（ML）和深度学习（DL）模型诊断各种癌症类型及近期进展进行了比较分析。在本研究中，我们纳入了四种癌症类型，即脑癌、肺癌、皮肤癌和乳腺癌，并使用ML和DL技术对其进行检测。在广泛的综述中，我们总共纳入了130篇文献，其中56篇基于ML，74篇来自基于DL的癌症检测技术。仅纳入了最近5年（2018 - 2023年）发表的经过同行评审的研究论文，基于发表年份、使用的特征、最佳模型、使用的数据集/图像以及最佳准确率等参数进行分析。我们分别回顾了基于ML和DL的癌症检测技术，并将准确率作为性能评估指标，以在验证分类器效率时保持同质性。
在所有综述文献中，DL技术的最高准确率达到100%，而ML技术达到99.89%。使用DL和ML方法实现的最低准确率分别为70%和75.48%。在皮肤癌检测中，表现最佳和最差的模型之间的准确率差异约为28.8%。此外，还介绍了使用ML和DL技术对每种癌症检测的关键发现和挑战。为了未来的研究目的，提供了表现最佳和最差的模型之间的比较分析，以及总体关键发现和挑战。尽管分析基于准确率作为性能指标和各种参数，但结果表明分类效率仍有显著的提升空间。
本文得出结论，ML和DL技术在早期检测各种癌症类型方面都有前景。然而，该研究确定了在临床环境中广泛应用这些技术需要解决的特定挑战。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，强调了需要在基于ML和DL的方法上持续进步，以提高诊断准确性并最终挽救更多生命。
