Machine Learning in Medical Image Processing: Review of Methods and Outcomes

Syaharuddin Syaharuddin

Abstract


This study uses a qualitative research method using a Systematic Literature Review (SLR) approach to comprehensively analyze optimal machine learning methodologies for medical image processing. The goal is to propose strategies to overcome existing barriers, thereby improving diagnostic accuracy and streamlining clinical workflows in healthcare through advanced machine learning applications. A literature search was conducted using three main data sources: Scopus, DOAJ, and Google Scholar, covering the period 2013 to 2024. Extensive application of machine learning (ML) techniques, especially deep learning models such as convolutional neural networks (CNN), has resulted in progress which is significant in medical image processing. These techniques have improved diagnostic accuracy and efficiency, overcome complex imaging challenges, and provided a powerful framework for disease detection, classification, and segmentation. This review aims to consolidate these findings and suggest future research directions to further integrate ML in medical imaging.


Keywords


Machine Learning; Medical and Image Processing

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References


E. Elyan et al., “Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward,” Artif. Intell. Surg., 2022, doi: 10.20517/ais.2021.15.

P. Lambin et al., “Radiomics: The bridge between medical imaging and personalized medicine,” Nature Reviews Clinical Oncology. 2017. doi: 10.1038/nrclinonc.2017.141.

L. P. et al., “Radiomics: The bridge between medical imaging and personalized medicine,” Nat. Rev. Clin. Oncol., 2017.

H. Malik, M. S. Farooq, A. Khelifi, A. Abid, J. Nasir Qureshi, and M. Hussain, “A Comparison of Transfer Learning Performance Versus Health Experts in Disease Diagnosis from Medical Imaging,” IEEE Access, 2020, doi: 10.1109/ACCESS.2020.3004766.

K. K. L. Wong, G. Fortino, and D. Abbott, “Deep learning-based cardiovascular image diagnosis: A promising challenge,” Futur. Gener. Comput. Syst., 2020, doi: 10.1016/j.future.2019.09.047.

A. Yousaf Gill, A. Saeed, S. Rasool, A. Husnain, and H. Khawar Hussain, “Revolutionizing Healthcare: How Machine Learning is Transforming Patient Diagnoses - a Comprehensive Review of AI’s Impact on Medical Diagnosis,” J. World Sci., 2023, doi: 10.58344/jws.v2i10.449.

N. Dhungel, G. Carneiro, and A. P. Bradley, Deep Learning and Convolutional Neural Networks for Medical Image Computing. 2017.

W. Zhang et al., “BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotation,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2022. doi: 10.1109/CVPR52688.2022.02001.

Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, “Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks,” IEEE Trans. Geosci. Remote Sens., 2016, doi: 10.1109/TGRS.2016.2584107.

Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, “A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects,” IEEE Trans. Neural Networks Learn. Syst., 2022, doi: 10.1109/TNNLS.2021.3084827.

I. Sánchez Fernández and J. M. Peters, “Machine learning and deep learning in medicine and neuroimaging,” Ann. Child Neurol. Soc., 2023, doi: 10.1002/cns3.5.

M. A. -, D. P. -, and H. S. -, “Machine Learning in Cardiology: a Survey of Early Detection Models for Heart Diseases,” Int. J. Multidiscip. Res., 2023, doi: 10.36948/ijfmr.2023.v05i03.3113.

A. A. Adekunle, O. B. Joseph, and A. M. Olalekan, “Early Parkinson’s Disease Detection Using Machine Learning Approach,” Asian J. Res. Comput. Sci., 2023, doi: 10.9734/ajrcos/2023/v16i2337.

H. Cui, L. Hu, and L. Chi, “Advances in Computer-Aided Medical Image Processing,” Applied Sciences (Switzerland). 2023. doi: 10.3390/app13127079.

B. M. Rashed and N. Popescu, “Performance Investigation for Medical Image Evaluation and Diagnosis Using Machine-Learning and Deep-Learning Techniques,” Computation, 2023, doi: 10.3390/computation11030063.

S. M. Anwar, M. Majid, A. Qayyum, M. Awais, M. Alnowami, and M. K. Khan, “Medical Image Analysis using Convolutional Neural Networks: A Review,” Journal of Medical Systems. 2018. doi: 10.1007/s10916-018-1088-1.

X. Yu, J. Wang, Q. Q. Hong, R. Teku, S. H. Wang, and Y. D. Zhang, “Transfer learning for medical images analyses: A survey,” Neurocomputing, 2022, doi: 10.1016/j.neucom.2021.08.159.

H. R. Roth, N. Rieke, S. Albarqouni, and Q. Li, “Guest Editorial Special Issue on Federated Learning for Medical Imaging: Enabling Collaborative Development of Robust AI Models,” IEEE Trans. Med. Imaging, 2023, doi: 10.1109/TMI.2023.3278528.

H. Kondylakis et al., “Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects,” Eur. Radiol. Exp., 2023, doi: 10.1186/s41747-023-00336-x.

N. Dong, M. Kampffmeyer, I. Voiculescu, and E. Xing, “Federated Partially Supervised Learning With Limited Decentralized Medical Images,” IEEE Trans. Med. Imaging, 2023, doi: 10.1109/TMI.2022.3231017.

Y. Singh et al., “Topological data analysis in medical imaging: current state of the art,” Insights into Imaging. 2023. doi: 10.1186/s13244-023-01413-w.

A. Elmahalawy and G. Abdel-Aziz, “Machine Learning in Medical Image Processing,” in Lecture Notes in Electrical Engineering, 2022. doi: 10.1007/978-981-19-2456-9_93.

S. Iqbal, A. N. Qureshi, J. Li, and T. Mahmood, “On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks,” Arch. Comput. Methods Eng., 2023, doi: 10.1007/s11831-023-09899-9.

C. S. Lee and A. Y. Lee, “How artificial intelligence can transform randomized controlled trials,” Translational Vision Science and Technology. 2020. doi: 10.1167/tvst.9.2.9.

M. T. K. Law et al., “Machine learning in secondary progressive multiple sclerosis: an improved predictive model for short-term disability progression,” Mult. Scler. J. - Exp. Transl. Clin., 2019, doi: 10.1177/2055217319885983.

V. Rodriguez-Romero, R. F. Bergstrom, B. S. Decker, G. Lahu, M. Vakilynejad, and R. R. Bies, “Prediction of Nephropathy in Type 2 Diabetes: An Analysis of the ACCORD Trial Applying Machine Learning Techniques,” Clin. Transl. Sci., 2019, doi: 10.1111/cts.12647.

A. Ezzati and R. B. Lipton, “Machine learning predictive models can improve efficacy of clinical trials for Alzheimer’s disease,” J. Alzheimer’s Dis., 2020, doi: 10.3233/JAD-190822.

N. Agarwal, N. Kumar, Anushka, V. Abrol, and Y. Garg, “Enhancing Image Recognition: Leveraging Machine Learning on Specialized Medical Datasets,” EAI Endorsed Trans. Pervasive Heal. Technol., 2023, doi: 10.4108/eetpht.9.4336.

S. Nazir and M. Kaleem, “Federated Learning for Medical Image Analysis with Deep Neural Networks,” Diagnostics. 2023. doi: 10.3390/diagnostics13091532.

A. Alwiyah and W. Setyowati, “A Comprehensive Survey of Machine Learning Applications in Medical Image Analysis for Artificial Vision,” Int. Trans. Artif. Intell., 2023, doi: 10.33050/italic.v2i1.438.

J. Ker, L. Wang, J. Rao, and T. Lim, “Deep Learning Applications in Medical Image Analysis,” IEEE Access, 2017, doi: 10.1109/ACCESS.2017.2788044.

B. J. Erickson, P. Korfiatis, Z. Akkus, and T. L. Kline, “Machine learning for medical imaging,” Radiographics, 2017, doi: 10.1148/rg.2017160130.

M. Chaudhary and H. Agrawal, “Challenges and Opportunities in Integrating Machine Learning with Medical Imaging: A Comprehensive Review,” in Proceedings of 2023 2nd International Conference on Informatics, ICI 2023, 2023. doi: 10.1109/ICI60088.2023.10421032.

R. J. Al Gharrawi and A. A. Al-Joda, “A Survey of Medical Image Analysis Based on Machine Learning Techniques,” J. Al-Qadisiyah Comput. Sci. Math., 2023, doi: 10.29304/jqcm.2023.15.1.1139.

M. L. Giger, “Machine Learning in Medical Imaging,” J. Am. Coll. Radiol., 2018, doi: 10.1016/j.jacr.2017.12.028.

V. Nittas et al., “Beyond high hopes: A scoping review of the 2019–2021 scientific discourse on machine learning in medical imaging,” PLOS Digit. Heal., 2023, doi: 10.1371/journal.pdig.0000189.

J. Huang, G. Galal, M. Etemadi, and M. Vaidyanathan, “Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review,” JMIR Medical Informatics. 2022. doi: 10.2196/36388.

T. V. Maliamanis, K. D. Apostolidis, and G. A. Papakostas, “How Resilient Are Deep Learning Models in Medical Image Analysis? The Case of the Moment-Based Adversarial Attack (Mb-AdA),” Biomedicines, 2022, doi: 10.3390/biomedicines10102545.

C. An, Y. W. Park, S. S. Ahn, K. Han, H. Kim, and S. K. Lee, “Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results,” PLoS One, 2021, doi: 10.1371/journal.pone.0256152.

P. Rajpurkar, E. Chen, O. Banerjee, and E. J. Topol, “AI in health and medicine,” Nature Medicine. 2022. doi: 10.1038/s41591-021-01614-0.

M. Nawaz, T. Nazir, J. Baili, M. A. Khan, Y. J. Kim, and J. H. Cha, “CXray-EffDet: Chest Disease Detection and Classification from X-ray Images Using the EfficientDet Model,” Diagnostics, 2023, doi: 10.3390/diagnostics13020248.

A. Kumar, L. Bi, J. Kim, and D. D. Feng, “Machine learning in medical imaging,” in Biomedical Information Technology, 2019. doi: 10.1016/B978-0-12-816034-3.00005-5.

P. Furtado, “Testing segmentation popular loss and variations in three multiclass medical imaging problems,” J. Imaging, 2021, doi: 10.3390/jimaging7020016.

Q. Wang, Y. Shi, and Di. Shen, “Machine Learning in Medical Imaging,” IEEE Journal of Biomedical and Health Informatics. 2019. doi: 10.1109/JBHI.2019.2920801.

C. Davatzikos et al., “Precision diagnostics based on machine learning-derived imaging signatures,” Magn. Reson. Imaging, 2019, doi: 10.1016/j.mri.2019.04.012.




DOI: https://doi.org/10.24114/cess.v9i2.61460

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This work is licensed under a Creative Commons Attribution 4.0 International License.

CESS (Journal of Computer Engineering, System and Science)

Creative Commons License
CESS (Journal of Computer Engineering, System and Science) is licensed under a Creative Commons Attribution 4.0 International License