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ARTICLE
UNLOCKING HEALTHCARE'S POTENTIAL: A COMPREHENSIVE REVIEW OF DEEP LEARNING METHODOLOGIES AND THEIR DIVERSE APPLICATIONS
Issue Vol. 1 No. 01 (2024): Volume 01 Issue 01 --- Section Articles --- Published Date: 2024-12-30
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.
Keywords
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.
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