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A depth analysis of recent innovations in non-invasive techniques using artificial intelligence approach for cancer prediction

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Abstract

The fight against cancer, a relentless global health crisis, emphasizes the urgency for efficient and automated early detection methods. To address this critical need, this review assesses recent advances in non-invasive cancer prediction techniques, comparing conventional machine learning (CML) and deep neural networks (DNNs). Focusing on these seven major cancers, we analyze 310 publications spanning the years 2018 to 2024, focusing on detection accuracy as the key metric to identify the most effective predictive models, highlighting critical gaps in current methodologies, and suggesting directions for future research. We further delved into factors like datasets, features, and modalities to gain a comprehensive understanding of each approach’s performance. Separate review tables for each cancer type and approach facilitated comparisons between top performers (accuracy exceeding 99%) and low performers (65.83 to 85.8%). Our exploration of public databases and commonly used classifiers revealed that optimal combinations of features, datasets, and models can achieve up to 100% accuracy for both CML and DNN. However, significant variations in accuracy (up to 35%) were observed, particularly when optimization was lacking. Notably, colorectal cancer exhibited the lowest accuracy (DNN 69%, CML 65.83%). A five-point comparative analysis (best/worst models, performance gap, average accuracy, and research trends) revealed that while DNN research is gaining momentum, CML approaches remain competitive, even outperforming DNN in some cases. This study presents an in-depth comparative analysis of CML and DNN techniques for cancer detection. This knowledge can inform future research directions and contribute to the development of increasingly accurate and reliable cancer detection tools.

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Authors and Affiliations

  1. School of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-Gu, Seongnam-Si, 13120, Gyeonggi-Do, Republic of Korea

    Hari Mohan Rai & Joon Yoo

  2. Department of Cyber Security, Information Processing and Storage, Satbayev University, Almaty, Kazakhstan

    Abdul Razaque

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  1. Hari Mohan Rai
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  2. Joon Yoo
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11517_2024_3158_MOESM1_ESM.docx

Supplementary file1 Appendix A: This appendix contains all the tables used in this study. Each table is referenced in the main text as Table A1, Table A2, and so on. (DOCX 1.28 MB)

11517_2024_3158_MOESM2_ESM.docx

Supplementary file2 Appendix B: Appendix B contains selected figures, cited in the main text as Figure B1, Figure B2, and so on. Additionally, this appendix provides a brief description of “Section 3.5 Classifiers,” covering the most commonly utilized conventional machine learning (CML) and deep learning (DL) classifiers. (DOCX 1284 KB)

11517_2024_3158_MOESM3_ESM.docx

Supplementary file3 Appendix C: This appendix focuses on a detailed analysis of the best and worst performing CML and DL classifiers across all cancer types explored in the study. (DOCX 50 KB)

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Rai, H.M., Yoo, J. & Razaque, A. A depth analysis of recent innovations in non-invasive techniques using artificial intelligence approach for cancer prediction. Med Biol Eng Comput 62, 3555–3580 (2024). https://doi.org/10.1007/s11517-024-03158-0

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