Special Issue "Healthcare Intelligence using Deep Learning and Computer Vision"

Submission Deadline: 31 January 2022 (closed)
Guest Editors
Dr. Balasundaram A, Vellore Institute of Technology (VIT), India.

Dr. Arun Kumar Sivaraman, Vellore Institute of Technology (VIT), India.

Dr. Kong Fah Tee, University of Greenwich, United Kingdom.

Dr. Arun Rajesh, University of Technology and Applied Sciences, Sultanate of Oman.


Over the last couple of decades, the world has shifted its attention towards smart decision making and automation of various day to day activities. Artificial Intelligence (AI) is considered as a primary reason for such automations and advancements. Healthcare is one of the prominent domains that lays emphasis on technology advancements and its fullest utilization across various applications. Hence, it is no wonder that healthcare domain has embraced AI to develop smart applications, devices and intelligent healthcare systems Such healthcare intelligence enabled systems paves way for better healthcare solutions and enhances the patient satisfaction quotient while at the same time offloads a major portion of challenging work performed by healthcare professionals. While it is known that Artificial Intelligence is a confluence of several technology enablers such as machine learning, deep learning, computer vision, data analytics, smart image and signal processing, statistics, robotics, IoT etc., deep learning and computer vision are considered as major contributors along with IoT.


Deep Learning (DL) is an advanced class of Machine Learning (ML) that uses multi layered neural networks that are capable of analyzing the input data and making decisions much similar to the way human brain does. DL finds its use across several applications ranging from simple virtual assistants, autonomous vehicles, translators, smart chatbots, personalized entertainment, shopping etc., to advanced health care solutions such as cancer analysis, gene sequence analysis, disease diagnosis and prediction, brain tumor diagnosis etc.


Computer Vision (CV) is predominantly used for analyzing data which is in the form of images or videos. When considered for healthcare domain, computer vision has a plethora of opportunities and is mainly used for medical image analysis of MRIs, X-Rays, CAT scans etc. CV thus reduces the effort of healthcare professionals in analyzing these images which are most commonly analyzed for diagnosing and treating diseases like Cancer, Brain tumor and disorder, chest infections, COVID-19 Sars2, Cognitive disorders, etc.


This special issue is focused towards providing an ambient platform for the researchers and practitioners to exchange their innovative and novel ideas pertaining to the use of DL and CV to impart intelligence across healthcare domain and to promote development of automated and smart healthcare applications.


Manuscripts are welcomed on the following topics but are not confined to:


- Deep Learning and Computer Vision based disease diagnosis

- Medical image analysis using Deep Learning and Computer Vision

- AI based health care decision support systems

- AI based smart patient monitoring systems

- Medical signal processing using Deep Learning and Computer Vision

- AI based healthcare recommendation systems

- AI based telemedicine and healthcare

- Cognitive disease predictions using Deep Learning and Computer Vision

- AI based pervasive and ubiquitous smart healthcare systems

- AI based smart hospital management systems

- COVID-19 prediction using Deep Learning and Computer Vision

- AI based smart surveillance enhancing healthcare security

Deep Learning, Computer Vision, Artificial Intelligence, Medical Image Processing, Medical Signal Processing, Healthcare Analytics

Published Papers
  • Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals
  • Abstract Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We… More
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  • Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19
  • Abstract The global pandemic of novel coronavirus that started in 2019 has seriously affected daily lives and placed everyone in a panic condition. Widespread coronavirus led to the adoption of social distancing and people avoiding unnecessary physical contact with each other. The present situation advocates the requirement of a contactless biometric system that could be used in future authentication systems which makes fingerprint-based person identification ineffective. Periocular biometric is the solution because it does not require physical contact and is able to identify people wearing face masks. However, the periocular biometric region is a small area, and extraction of the required… More
  •   Views:902       Downloads:558       Cited by:1        Download PDF