A Review on Medical Image Applications Based on Deep Learning Techniques

Basit Öğe Kaydı
dc.authoridIndrit Myderrizi / 0000-0002-2112-7911
dc.authorscopusidIndrit Myderrizi / 58578388200
dc.authorwosidIndrit Myderrizi / A-1247-2011
dc.contributor.authorAbdulwahhab, Ali H
dc.contributor.authorMahmood, Noof T.
dc.contributor.authorMohammed, Ali Abdulwahhab
dc.contributor.authorMyderrizi, Indrit
dc.contributor.authorAl-Jumaili, Mustafa Hamid
dc.date.accessioned2025-04-18T07:53:51Z
dc.date.available2025-04-18T07:53:51Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü
dc.description.abstractThe 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. © 2024 by the authors.
dc.identifier.citationAbdulwahhab, 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. J. Image Graph., 12(3), 215.
dc.identifier.doi10.18178/JOIG.12.3.215-227
dc.identifier.endpage227
dc.identifier.issn23013699
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85201556291
dc.identifier.scopusqualityQ2
dc.identifier.startpage215
dc.identifier.urihttp://dx.doi.org/10.18178/JOIG.12.3.215-227
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6500
dc.identifier.volume12
dc.indekslendigikaynakScopus
dc.institutionauthorMyderrizi, Indrit
dc.institutionauthoridIndrit Myderrizi / 0000-0002-2112-7911
dc.language.isoen
dc.publisherUniversity of Portsmouth
dc.relation.ispartofJournal of Image and Graphics(United Kingdom)
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectDeep Learning
dc.subjectHigh-Quality Medical İmage Datasets
dc.subjectMachine Learning
dc.subjectMedical İmage Analysis
dc.titleA Review on Medical Image Applications Based on Deep Learning Techniques
dc.typeArticle

Dosyalar

Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Elektrik-Elektronik Mühendisliği Bölümü Makale Koleksiyonu