Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges
Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount
of raw data into beneficial medical decisions for treatment and care has increased in …
of raw data into beneficial medical decisions for treatment and care has increased in …
Analysis of deep learning techniques for prediction of eye diseases: A systematic review
The prediction and early diagnosis of eye diseases are critical for effective treatment and
prevention of vision loss. The identification of eye diseases has recently been the subject of …
prevention of vision loss. The identification of eye diseases has recently been the subject of …
A deep learning based framework for diagnosis of mild cognitive impairment
Detecting mild cognitive impairment (MCI) from electroencephalography (EEG) data is a
challenging problem as existing methods rely on machine learning based shallow …
challenging problem as existing methods rely on machine learning based shallow …
Antisocial Behavior Identification from Twitter Feeds Using Traditional Machine Learning Algorithms and Deep Learning.
Antisocial behavior (ASB) is one of the ten personality disorders included in 'The Diagnostic
and Statistical Manual of Mental Disorders (DSM-5) and falls in the same cluster as …
and Statistical Manual of Mental Disorders (DSM-5) and falls in the same cluster as …
Automatic breast lesion segmentation in phase preserved DCE-MRIs
We offer a framework for automatically and accurately segmenting breast lesions from
Dynamic Contrast Enhanced (DCE) MRI in this paper. The framework is built using max flow …
Dynamic Contrast Enhanced (DCE) MRI in this paper. The framework is built using max flow …
Patient assignment optimization in cloud healthcare systems: a distributed genetic algorithm
Integrating Internet technologies with traditional healthcare systems has enabled the
emergence of cloud healthcare systems. These systems aim to optimize the balance …
emergence of cloud healthcare systems. These systems aim to optimize the balance …
Graph intelligence enhanced bi-channel insider threat detection
For an organization, insider intrusion generally poses far more detrimental threats than
outsider intrusion. Traditionally, insider threat is detected by analyzing logged user …
outsider intrusion. Traditionally, insider threat is detected by analyzing logged user …
Early detection of paediatric and adolescent obsessive–compulsive, separation anxiety and attention deficit hyperactivity disorder using machine learning algorithms
Purpose Mental health issues of young minds are at the threshold of all development and
possibilities. Obsessive–compulsive disorder (OCD), separation anxiety disorder (SAD), and …
possibilities. Obsessive–compulsive disorder (OCD), separation anxiety disorder (SAD), and …
An efficient approach to predict eye diseases from symptoms using machine learning and ranker-based feature selection methods
The eye is generally considered to be the most important sensory organ of humans.
Diseases and other degenerative conditions of the eye are therefore of great concern as …
Diseases and other degenerative conditions of the eye are therefore of great concern as …
Eye diseases diagnosis using deep learning and multimodal medical eye imaging
The present study carries out an empirical evaluation and comparison of the seven most
recent deep Convolutional Neural Network (CNN) techniques (VGG19, DenseNet121 …
recent deep Convolutional Neural Network (CNN) techniques (VGG19, DenseNet121 …