Call for Papers: 3D Image Based Medical Decision Support Systems using Deep Learning Approaches

Guest Editors:  

(1) Dr. D. Jude Hemanth (Lead Guest Editor)
Department of ECE
Karunya University, India
E-mail :

(2) Prof. Jacek Zurada
Department of Electrical and Computer Engineering
University of Louisville, USA  

(3)  Prof George K. Karagiannidis
Department of Electrical and Computer Engineering
Aristotle University of Thessaloniki, Greece

Medical industry is highly dependent on “images” due to the rapid development of biomedical imaging technologies. Specifically, diagnosis of human body abnormalities is normally carried out by physicians with the help of images. In recent past, computational intelligence-based methodologies are used to assist the physicians in accurate diagnosis of the abnormalities. However, these conventional approaches for disease diagnosis has two drawbacks: (1) The 2D medical data is usually insufficient in decision making by the physicians. (2) The intelligence-based machine learning techniques are less accurate in identifying the abnormalities of human body.

These problems can be tackled to some extent with the help of higher dimensional volumetric images and deep learning methodologies. However, there are plenty of challenges associated with them for real-time applications. One among them is the computational complexity which increases with increase in the dimensions of the images. Another aspect is the huge complexity of deep learning approaches. Thus, there is a significant necessity for research in these areas to provide solutions for such open problems. This special issue specifically focuses on innovative research concepts involving 3D images and deep learning approaches for human body disorder identification. The outcome of this issue will definitely lead to some sparks in the minds of readers/practitioners/researchers to develop some real-time solutions for the human society in the context of medical image processing.

Topics of interest:

This special issue shall focus on the following two parts:
(A) Application (but not limited to):

  • 3D brain image analysis for Alzheimer disease identification
  • 3D image segmentation for breast cancer diagnosis
  • 3D retinal image enhancement for abnormality identification in eye
  • 3D image classification for detection of cardiac diseases
  • 3D colour image clustering
  • Foetal abnormality detection from 3D ultrasound images
  • 3D visualization of human anatomy
  • Morphological image processing on 3D medical data
  • 3D image processing for facial surgery applications
  • Any other biomedical applications with 3D data

(B) Methodology (but not limited to):

  • Deep convolutional neural networks for 3D medical data analysis
  • Deep belief networks for 3D medical data analysis
  • Deep neural networks for 3D medical data analysis
  • Auto encoders for 3D medical data analysis
  • Generative Adversarial networks for 3D medical data analysis
  • Hybrid approaches for 3D medical data analysis
  • Modified neural networks for 3D medical data analysis
  • Recurrent neural networks for 3D medical data analysis
  • Recursive neural networks for 3D medical data analysis
  • Any other deep learning approaches for 3D medical data analysis

Submission Procedure:
Guidelines for authors can be found at Prospective authors should submit high quality and original manuscripts. All papers will undergo the same rigorous MSSP review process. Please refer to the MSSP website for detailed instructions on paper submission. Please choose “SI: 3D image based medical decision support systems” as the Article Type in the dropdown menu of the submission link.Tentative Deadlines:
Initial manuscript submission: 31st August, 2020
First round of reviews: 31st December, 2020For any clarification, kindly contact the lead guest editor at : (OR) +91-9443001874.