Guest Editors
Dr. Ahmed Shaffie
Email: ashaffie@lsua.edu
Affiliation: Mathematics and Computer Science Department, Louisiana State University of Alexandria, Louisiana, 71302, United States
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Research Interests: Medical Imaging, Non-invasive Computer-assisted Diagnosis Systems, Machine Learning, Artificial Intelligence, and Pattern Recognition.
Summary
Medical imaging plays a crucial role in diagnosing and treating numerous diseases. Traditionally, this field has relied heavily on the expertise of radiologists to interpret complex images and provide accurate diagnoses. However, in recent years, there has been a significant shift towards incorporating machine learning (ML) and artificial intelligence (AI) technologies into medical imaging. These advancements have the potential to automate and refine image analysis, reducing the burden on radiologists and improving overall diagnostic accuracy. The integration of ML and AI has led to significant advancements in the field, enhancing diagnostic precision and patient outcomes. This special issue aims to provide a comprehensive overview of the current advancements and technologies in this area, highlighting the latest innovations that enable earlier, more accurate diagnoses and personalized treatment plans. By addressing the growing need for efficient diagnostic tools in healthcare, this issue seeks to showcase the transformative impact of ML and AI on medical imaging, offering valuable insights for researchers, clinicians, and healthcare professionals.
The Special Issue topics include, but are not limited to the following:
· Developing AI-powered computer-aided diagnosis systems
· Machine learning approaches for analyzing medical images
· AI-based ECG analysis for computer-aided diagnosis
· Deep learning for instance and semantic segmentation in medical images
· Classification techniques for medical imaging diagnosis
· Deep learning for feature extraction and image analysis in medical imaging
· Handcrafted features extraction methods for medical imaging
· Combining deep learning and handcrafted features for enhanced medical imaging analysis
· Semi-supervised and transfer learning for medical imaging
· Multimodal medical image fusion using deep learning techniques
Keywords
Machine Learning (ML), Artificial Intelligence (AI), Computer-Aided Diagnosis (CAD), Medical Imaging Analysis, Segmentation, Diagnosis, Deep Learning, Imaging Modalities
Published Papers
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