Medical Imaging Decision Support Systems Using Deep Learning and Machine Learning Algorithms

Submission Deadline: 30 April 2025 (closed) View: 1559 Submit to Special Issue

Guest Editors

Assistant Prof. Dr. Saadaldeen Rashid Ahmed

Email:saadaljanabi78@gmail.com

Affiliation: Computer Science , Bayan University, Erbil, 44001, Iraq

Homepage:

Research Interests: Image Processing, Computer Vision, Deep Learning , Machine Learning, BCI 

Assistant Prof. Dr. Lal Hussain

Email:lal.hussain@ajku.edu.pk

Affiliation: Department of Computer Science, University of Azad Jammu and Kashmir, Pakistan

Homepage:

Research interest: Signal and image processing, Complexity Analysis, Machine learning, deep learning, AI, Pattern Recognition, Nonlinear Dynamical Analysis, Bayesian Analysis

Dr. Mohammed Thakir ALmashhadany

Email:mohammed1991almashhadany@gmail.com

Affiliation: Altinbas University , Al-Maarif University

Homepage:

Research Interests: Image Processing, Computer Vision, Deep Learning , Machine Learning

Summary

In healthcare, medical imaging analysis applied to medical imaging from X-ray, MRI, CT, PET, and fMRI scans, combined with image processing techniques like pre-processing, enhancement, segmentation, registration, restoration, and morphological processing, can significantly aid radiologists, clinicians, and healthcare practitioners in diagnosis, disease progression monitoring, staging, recurrence prediction, survival analysis, and severity assessment.

Medical imaging systems and decision support systems driven by AI and machine learning algorithms can empower clinicians to deliver cost-effective and improved care by providing patient-specific information and integrating evidence-based knowledge, ultimately leading to timelier, well-informed clinical decisions and better healthcare outcomes.

This topical collection focuses on advancements and applications of deep learning and machine learning algorithms in healthcare monitoring systems, clinical decision support systems, and industrial expert decision systems. Potential topics include, but are not limited to:

- Developing robust AI-based predictive models for various medical disorders

- Pattern recognition applications in healthcare systems

- Utilizing MRI, CT, and PET images for improved tumor stage prediction

- Machine and deep learning techniques for predicting survival and disease severity based on radiology and pathology images

- Multiparametric approaches for predicting disease progression in healthcare settings

- Efficient cardiovascular disease prediction using feature extraction and selection techniques

- Implementing profitable industrial applications through pattern recognition algorithms

Authors are invited to submit original research articles, review papers, case studies, and perspectives that address the theme of the topical collection. Submissions will undergo a rigorous peer-review process to ensure the highest quality and relevance to the scope of the collection.

Keywords

Published Papers