Guest Editors
Prof. Batyrkhan Omarov, Al-Farabi Kazakh National University, Kazakhstan
Prof. Aigerim Altayeva, International Information Technology University，Kazakhstan
Prof. Bakhytzhan Omarov, International University of Tourism and Hospitality, Kazakhstan
Summary
In this special issue, we delve into the cutting-edge advancements and transformative applications of deep learning techniques within the realms of biomedical engineering and healthcare. Deep learning, a subset of artificial intelligence, has emerged as a groundbreaking tool, offering unparalleled capabilities in interpreting complex biomedical signals and images. This issue brings together a collection of research articles, reviews, and case studies that highlight the innovative integration of deep learning methodologies for analyzing physiological signals (such as EEG, ECG, and EMG) and medical images (including MRI, CT scans, X-rays, and etc.).
The content spans a broad spectrum, from theoretical frameworks and algorithm development to practical applications and case studies, providing insights into the current state-of-the-art and future directions in this rapidly evolving field. Key themes include, but are not limited to, the development of novel deep learning models for disease diagnosis and prognosis, enhancement of image quality and interpretation, real-time monitoring and analysis of biomedical signals, and personalized healthcare solutions.
Contributors to this issue showcase the significant impact of deep learning on improving diagnostic accuracy, enabling early detection of abnormalities, and facilitating personalized treatment plans. Furthermore, discussions extend to ethical considerations, data privacy, and the challenges of implementing AI technologies in clinical settings, offering a comprehensive overview of the landscape of deep learning applications in biomedical signal and image processing.
Through a blend of technical depth and accessibility, this special issue aims to inform and inspire researchers, clinicians, and industry professionals about the potential of deep learning to revolutionize healthcare, paving the way for more innovative, efficient, and personalized medical care.
Keywords
Deep Learning
Biomedical Signal Processing
Medical Image Processing
Artificial Intelligence in Healthcare
EEG, ECG, EMG Analysis
MRI, CT, X-ray Imaging
Neural Networks
Health Monitoring Systems
Predictive Analytics in Medicine
Diagnostic Imaging Techniques
Image Segmentation Algorithms
Deep Learning for Oncology
Cardiovascular Disease Detection
Neurological Disorder Analysis
Wearable Technology for Health Monitoring
Telemedicine and Remote Diagnostics
Rehabilitation Robotics
Published Papers
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