Machine learning in computer vision: a review

AA Khan, AA Laghari, SA Awan - EAI Endorsed Transactions on …, 2021 - publications.eai.eu
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 …

A literature review of early-stage diabetic retinopathy detection using deep learning and evolutionary computing techniques

S Bhandari, S Pathak, SA Jain - Archives of Computational Methods in …, 2023 - Springer
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 …

Sentiment analysis and topic modeling for COVID-19 vaccine discussions

H Yin, X Song, S Yang, J Li - World Wide Web, 2022 - Springer
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 …

Convolutional neural network for multi-class classification of diabetic eye disease

R Sarki, K Ahmed, H Wang, Y Zhang… - … Transactions on Scalable …, 2021 - vuir.vu.edu.au
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 …

Image preprocessing in classification and identification of diabetic eye diseases

R Sarki, K Ahmed, H Wang, Y Zhang, J Ma… - Data Science and …, 2021 - Springer
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 …

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 …

A lightweight robust deep learning model gained high accuracy in classifying a wide range of diabetic retinopathy images

MAK Raiaan, K Fatema, IU Khan, S Azam… - IEEE …, 2023 - ieeexplore.ieee.org
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 …

Automated detection of COVID-19 through convolutional neural network using chest x-ray images

R Sarki, K Ahmed, H Wang, Y Zhang, K Wang - Plos one, 2022 - journals.plos.org
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 …

A deep learning based framework for diagnosis of mild cognitive impairment

AM Alvi, S Siuly, H Wang, K Wang… - Knowledge-Based Systems, 2022 - Elsevier
Detecting mild cognitive impairment (MCI) from electroencephalography (EEG) data is a
challenging problem as existing methods rely on machine learning based shallow …

Btc-fcnn: fast convolution neural network for multi-class brain tumor classification

BS Abd El-Wahab, ME Nasr, S Khamis… - … information science and …, 2023 - Springer
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 …