Machine learning in computer vision: a review
INTRODUCTION: Due to the advancement in the field of Artificial Intelligence (AI), the ability
to tackle entire problems of machine intelligence. Nowadays, Machine learning (ML) is …
to tackle entire problems of machine intelligence. Nowadays, Machine learning (ML) is …
A literature review of early-stage diabetic retinopathy detection using deep learning and evolutionary computing techniques
Soft computing approaches are contributing to various areas of real-world problems. These
techniques are being used in optimization problems, feature selection, classification as well …
techniques are being used in optimization problems, feature selection, classification as well …
Sentiment analysis and topic modeling for COVID-19 vaccine discussions
The outbreak of the novel coronavirus disease (COVID-19) has been ongoing for almost two
years and has had an unprecedented impact on the daily lives of people around the world …
years and has had an unprecedented impact on the daily lives of people around the world …
Convolutional neural network for multi-class classification of diabetic eye disease
Prompt examination increases the chances of effective treatment of Diabetic Eye Disease
(DED) and reduces the likelihood of permanent deterioration of vision. A key tool commonly …
(DED) and reduces the likelihood of permanent deterioration of vision. A key tool commonly …
Image preprocessing in classification and identification of diabetic eye diseases
Diabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients.
Identifying DED is a crucial activity in retinal fundus images because early diagnosis and …
Identifying DED is a crucial activity in retinal fundus images because early diagnosis and …
Economics of artificial intelligence in healthcare: diagnosis vs. treatment
NN Khanna, MA Maindarkar, V Viswanathan… - Healthcare, 2022 - mdpi.com
Motivation: The price of medical treatment continues to rise due to (i) an increasing
population;(ii) an aging human growth;(iii) disease prevalence;(iv) a rise in the frequency of …
population;(ii) an aging human growth;(iii) disease prevalence;(iv) a rise in the frequency of …
A lightweight robust deep learning model gained high accuracy in classifying a wide range of diabetic retinopathy images
Diabetic retinopathy (DR) is a common complication of diabetes mellitus, and retinal blood
vessel damage can lead to vision loss and blindness if not recognized at an early stage …
vessel damage can lead to vision loss and blindness if not recognized at an early stage …
Automated detection of COVID-19 through convolutional neural network using chest x-ray images
The COVID-19 epidemic has a catastrophic impact on global well-being and public health.
More than 27 million confirmed cases have been reported worldwide until now. Due to the …
More than 27 million confirmed cases have been reported worldwide until now. Due to the …
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 …
Btc-fcnn: fast convolution neural network for multi-class brain tumor classification
Timely prognosis of brain tumors has a crucial role for powerful healthcare of remedy-
making plans. Manual classification of the brain tumors in magnetic resonance imaging …
making plans. Manual classification of the brain tumors in magnetic resonance imaging …