Computer Vision in Healthcare Applications

  title={Computer Vision in Healthcare Applications},
  author={Junfeng Gao and Yong Yang and Pan Lin and Dong Sun Park},
  journal={Journal of Healthcare Engineering},
College of Biomedical Engineering, South-Central University for Nationalities, Wuhan 430074, China Key Laboratory of Cognitive Science, State Ethnic Affairs Commission, Wuhan 430074, China Hubei Key Laboratory of Medical Information Analysis and Tumor Diagnosis & Treatment, Wuhan 430074, China School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330032, China IT Convergence Research Center, Chonbuk National University, Jeonju, Jeonbuk 54896, Republic of Korea 
Quantum algorithm for quicker clinical prognostic analysis: an application and experimental study using CT scan images of COVID-19 patients
A new image classification and segmentation method through simulation techniques, conducted over images of COVID-19 patients in India, introducing the use of Quantum Machine Learning (QML) in medical practice.
Deep Convolutional Neural Networks for Automated Diagnosis of Disc Herniation on Axial MRI
Experimental results prove that this deep CNN results enjoy an improvement compared to former proposed methods; the accuracy, sensitivity and specificity for random sub sampling method are 87.75, 86.5, and 94.75%, respectively, achieved by AlexNet architecture of CNN.
Computer vision based working environment monitoring to analyze Generalized Anxiety Disorder (GAD)
A novel computer vision assisted deep learning based posture monitoring system is proposed to predict Generalized Anxiety Disorder (GAD) oriented physical abnormalities of an individual from their working environment and the calculated outcomes show that the proposed methodology performs better contrasted with other contemporary studies for activity prediction, data processing cost, error rate, and time complexity.
A Comparison of Deep Learning Classification Methods on Small-scale Image Data set: from Convolutional Neural Networks to Visual Transformers
The application and characteristics of convolutional neural networks and visual transformers and the problems of somemodels are discussed, and the recommended deep learning model is given according to the model application environment.
Automatic Diagnosis of Disc Herniation in Two-Dimensional MR Images with Combination of Distinct Features Using Machine Learning Methods
This study used 50 clinical Magnetic Resonance Images (MRI) cases including 250 lumbar area discs to develop a diagnosis system and found the combination of effective features, which demonstrated an average of 97.91% and 97.08% accuracy with K-fold cross validation method.
Investigations of the Influences of a CNN's Receptive Field on Segmentation of Subnuclei of Bilateral Amygdalae
This study employed "AmygNet", a dual-branch fully convolutional neural network (FCNN) with two different sizes of receptive fields, to investigate the effects of receptive field on segmenting four major subnuclei of bilateral amygdalae.
Brain Tumors Presumed Malignant without Biopsy-Evaluation and Treatment Problems
A separate diagnostic and treatment protocol is proposed for this group of patients, based on laboratory (ctDNA) and imagistic criteria, that cannot receive oncological treatment or radiotherapy because of the restrictive indications of the insurers (health insurance companies) or the risk of allegations of malpractice.
Recent developments in computer vision based real-time monitoring in health and well-being
Current gesture recognition for realtime detection of human emotions and alertness, sign language translation, detection of safety critical incidents such as fall incident detection, functional vision aids for partially and fully blind persons, tele-surgery, computer vision based diagnostic health examination methods (endoscopy, photoplethysmography, and digital mammography), and computer visionbased aids within rehabilitation are presented.
Paired Augmentation for Improved Image Classification using Neural Network Models
This work presents a method to determine the most effective augmentation techniques to combine into the machine learning pipeline, and concludes that the pairing of augmentation methods results in improvements in the classification tasks.
Modeling Research Topics for Artificial Intelligence Applications in Medicine: Latent Dirichlet Allocation Application Study
The application of AI in medicine has grown rapidly and focuses on three leading platforms: clinical practices, clinical material, and policies, which might be one of the methods to narrow down the inequality in health care and medicine between developing and developed countries.