Systematic Survey of Deep Fuzzy Computer Vision in Biomedical Research
Baimukashev R. Kadyrov S. Turan C.
2024Tsinghua University Press
Fuzzy Information and Engineering
2024#16Issue 3220 - 243 pp.
This systematic survey explores the landscape of fuzzy computer vision techniques in biomedical research using articles from the Scopus database over the past decade. With a focus on methodologies, applications, and challenges, the survey aims to guide future research at the intersection of fuzzy logic and computer vision in biomedicine. Emphasizing applications such as dental image analysis and brain tumor detection, the paper showcases the collaborative potential of deep learning and fuzzy logic in enhancing biomedical image analysis. Despite notable advancements, challenges like model interpretability and scalability persist. The survey concludes by proposing future research directions, underscoring the pivotal role of fuzzy computer vision in advancing biomedical research.
algorithm , artificial intellegence , biomedical research , deep learning , deep neural networks , fuzzy computer vision , image analysis
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SDU University, Computer Science Department, Kaskelen, 040900, Kazakhstan
New Uzbekistan University, Department of General Education, Tashkent, 100000, Uzbekistan
SDU University
New Uzbekistan University
10 лет помогаем публиковать статьи Международный издатель
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