A review on medical image applications based on deep learning techniques
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Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
University of Portsmouth
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The integration of deep learning in medical image analysis is a transformative leap in healthcare, impacting diagnosis and treatment significantly. This scholarly review explores deep learning’s applications, revealing limitations in traditional methods while showcasing its potential. It delves into tasks like segmentation, classification, and enhancement, highlighting the pivotal roles of Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). Specific applications, like brain tumor segmentation and COVID-19 diagnosis, are deeply analyzed using datasets like NIH Clinical Center’s Chest X-ray dataset and BraTS dataset, proving invaluable for model training. Emphasizing high-quality datasets, especially in chest X-rays and cancer imaging, the article underscores their relevance in diverse medical imaging applications. Additionally, it stresses the managerial implications in healthcare organizations, emphasizing data quality and collaborative partnerships between medical practitioners and data scientists. This review article illuminates deep learning’s expansive potential in medical image analysis, a catalyst for advancing healthcare diagnostics and treatments.
Açıklama
Anahtar Kelimeler
Deep learning, High-quality medical image datasets, Machine learning, Medical image analysis
Kaynak
Journal of Image and Graphics(United Kingdom)
WoS Q Değeri
Scopus Q Değeri
Q2
Cilt
12
Sayı
3
Künye
Abdulwahhab, A. H., Mahmood, N. T., Mohammed, A. A., Myderrizi, I., Al-Jumaili, M. H. (2024). A review on medical image applications based on deep learning techniques. Journal of Image and Graphics(United Kingdom), 12(3), 215-227. 10.18178/JOIG.12.3.215-227
