UNLOCKING HEALTHCARE'S POTENTIAL: A COMPREHENSIVE REVIEW OF DEEP LEARNING METHODOLOGIES AND THEIR DIVERSE APPLICATIONS
Abstract
The integration of deep learning in healthcare is rapidly transforming various facets of the industry, offering innovative solutions to complex medical challenges. This comprehensive review explores the fundamental deep learning techniques and their extensive applications within the healthcare domain. We discuss key architectures such as Convolutional Neural Networks (CNNs) for medical image analysis, Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) for sequential data, Autoencoders (AEs) for dimensionality reduction, Restricted Boltzmann Machines (RBMs) for feature extraction, Deep Belief Networks (DBNs), and Generative Adversarial Networks (GANs) for data generation. The article highlights established successes in medical imaging and diagnostics, including automated segmentation in cardiac MRI [9], early detection of neurological disorders like Alzheimer's and Parkinson's diseases [10, 18, 19, 25], cancer detection [21, 26], and improved diagnosis of infectious diseases such as COVID-19 [11, 24]. Furthermore, we delve into the role of deep learning in disease diagnosis and predictive analytics, exemplified by diabetes detection [20], cardiovascular risk assessment [27], and proactive predictive medicine models [22]. The application of deep learning to Electronic Health Records (EHR) and Natural Language Processing (NLP) for medical record analysis [23] and information processing [5] is also examined. While acknowledging the significant advancements, the discussion addresses persistent challenges such as data availability, model interpretability, and ethical considerations. Future directions for deep learning in healthcare are also explored, emphasizing the need for robust, interpretable, and generalizable AI solutions. This review underscores deep learning's indispensable role in advancing precise, proactive, and patient-centric healthcare.
References
1. Amini, Mahyar, and Ali Rahmani. "Machine learning process evaluating damage classification of composites." International Journal of Science and Advanced Technology 9.12 (2023): 240-250.
2. Baduge, Shanaka Kristombu, et al. "Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications." Automation in Construction 141 (2022): 104440.
3. Amini, Mahyar, Koosha Sharifani, and Ali Rahmani. "Machine Learning Model Towards Evaluating Data gathering methods in Manufacturing and Mechanical Engineering." International Journal of Applied Science and Engineering Research 15.4 (2023): 349-362.
4. Abd Elaziz, Mohamed, et al. "Advanced metaheuristic optimization techniques in applications of deep neural networks: a review." Neural Computing and Applications (2021): 1-21.
5. Sharifani, Koosha and Amini, Mahyar and Akbari, Yaser and Aghajanzadeh Godarzi, Javad. "Operating Machine Learning across Natural Language Processing Techniques for Improvement of Fabricated News Model." International Journal of Science and Information System Research 12.9 (2022): 20-44.
6. Raghvendra Bhat, Sandya Mannarswamy, Shreyas N C, DL4HC: Deep Learning for Healthcare, CODS-COMAD 2023, January 04–07, 2023, Pages 327–329.
7. Shahab Shamshirband, Mahdis Fathi, Abdollah Dehzangi, Anthony Theodore Chronopoulos, Hamid Alinejad-Rokny, A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues, Journal of Biomedical Informatics, Volume 113, January 2021.
8. M. Cabrita, H. op den Akker, M. Tabak, H.J. Hermens, M.M. Vollenbroek-Hutten, Persuasive technology to support active and healthy ageing: An exploration of past, present, and future, J. Biomed. Inform. 84 (2018) 17-30.
9. M. Avendi, A. Kheradvar, H. Jafarkhani, A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI Medical. Image Analysis., 30 (2016), pp. 108-119.
10. F. Li, L. Tran, K.-H. Thung, S. Ji, D. Shen, J. Li, A robust deep model for improved classification of AD/MCI patients IEEE J. Biomed. Health. Inf., 19 (2015), pp. 1610-1616.
11. Al-Waisy, A.S.; Mohammed, M.A.; Al-Fahdawi, S.; Maashi, M.S.; Garcia-Zapirain, B.; Abdulkareem, K.H.; Mostafa, S.A.; Le, D.N. COVID-DeepNet: Hybrid Multimodal Deep Learning System for Improving COVID-19 Pneumonia Detection in Chest X-ray Images. Comput. Mater. Contin. 2021, 67, 2409–2429.
12. https://www.simplilearn.com/tutorials/deep-learning-tutorial/deep-learning-algorithm
13. Lakshmanna, K.; Kaluri, R.; Gundluru, N.; Alzamil, Z.S.; Rajput, D.S.; Khan, A.A.; Haq, M.A.; Alhussen, A. A Review on Deep Learning Techniques for IoT Data. Electronics 2022, 11, 1604.
14. Abdel-Jaber, Hussein, Disha Devassy, Azhar Al Salam, Lamya Hidaytallah, and Malak EL-Amir. 2022. "A Review of Deep Learning Algorithms and Their Applications in Healthcare" Algorithms 15, no. 2: 71.
15. Autoencoders Tutorial|What Are Autoencoders? Edureka. 12 October 2018. Available online: https://www.edureka.co/blog/autoencoders-tutorial/
16. Fischer, A.; Igel, C. An introduction to restricted Boltzmann machines. In Iberoamerican Congress on Pattern Recognition; Springer: Berlin/Heidelberg, Germany, 2012; pp. 14–36.
17. Hinton, G.E.; Salakhutdinov, R.R. Reducing the dimensionality of data with neural networks. Science 2006, 313, 504–507.
18. Janani Venugopalan, Li Tong, Hamid Reza Hassanzadeh & May D. Wang, “Multimodal deep learning models for early detection of Alzheimer’s disease stage”, 2021, Nature, Scientific reports, Article.
19. S. Sivaranjini & C. M. Sujatha, “Deep learning-based diagnosis of Parkinson’s disease using convolutional neural network”, 2019, Multimedia Tools and Applications, Springer.
20. Motiur Rahman, Dilshad Islam, Rokeya Jahan, Mukti, Indrajit Saha, “A deep learning approach based on convolutional LSTM for detecting diabetes”, 2020, Computational Biology and Chemistry, Volume 88, ELSEVIER.
21. Andre Esteva1, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau & Sebastian Thrun, “Dermatologist-level classification of skin cancer with deep neural networks”, 2017, Macmillan Publishers Limited, part of Springer Nature.
22. Trang Pham, Truyen Tran, Dinh Phung and Svetha Venkatesh, “DeepCare: A Deep Dynamic Memory Model for Predictive Medicine”, 2016, Springer International Publishing Switzerland.
23. Phuoc Nguyen, Truyen Tran, Nilmini Wickramasinghe, Svetha Venkatesh, “Deepr: A Convolutional Net for Medical Records”, 2017, IEEE Journal of Biomedical and Health Informatics, Volume: 21, Issue: 1.
24. O.S.Albahri, A.A.Zaidan, A.S. Albahri, B.B.Zaidan, Karrar Hameed Abdulkareem, “Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in term of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects”, 2020, Journal of Infection and Public Health, ELSEVIER.
25. Amina Naseer, Monail Rani, Saeeda Naz, Muhammad Imran Razzak, Muhammad Imran, Guandong Xu, “Refining Parkinson’s neurological disorder identification through deep transfer learning”, 2019, Neural Computing and Applications, Springer.
26. Pradeep Kumar Mallick, Seuc Ho Ryu, Sandeep Kumar Satapathy, Shruti Mishra, Gia Nhu Nguyen, Prayag Tiwari, “Brain MRI ImageClassification for Cancer Detection using Deep Wavelet Autoencoder based Deep Neural Network”, 2019, IEEE Access, Volume 7.
27. Giovanni Paragliola, Antonio Coronato, “An hybrid ECG-based deep network for the early identification of high-risk to major cardiovascular events for hypertension patients”, 2021, Journal of Biomedical Informatics, ELSEVIER.