Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods
The exponential increase in the number of diabetics around the world has led to an equally
large increase in the number of diabetic retinopathy (DR) cases which is one of the major …
large increase in the number of diabetic retinopathy (DR) cases which is one of the major …
Improving the accuracy of diabetes diagnosis applications through a hybrid feature selection algorithm
X Li, J Zhang, F Safara - Neural processing letters, 2023 - Springer
Artificial intelligence is a future and valuable tool for early disease recognition and support in
patient condition monitoring. It can increase the reliability of the cure and decision making by …
patient condition monitoring. It can increase the reliability of the cure and decision making by …
An automatic detection system of diabetic retinopathy using a hybrid inductive machine learning algorithm
Recently, the leading cause of preventable blindness is diabetic retinopathy (DR). Although
there are several undiagnosed and non-treated cases of DR, accurate and adequate retinal …
there are several undiagnosed and non-treated cases of DR, accurate and adequate retinal …
Early prediction of gestational diabetes with parameter-tuned K-Nearest Neighbor Classifier
TA Assegie, T Suresh, R Purushothaman… - Journal of Robotics …, 2023 - journal.umy.ac.id
Diabetes is one of the quickly spreading chronic diseases causing health complications,
such as diabetes retinopathy, kidney failure, and cardiovascular disease. Recently, machine …
such as diabetes retinopathy, kidney failure, and cardiovascular disease. Recently, machine …
Two-stage framework for diabetic retinopathy diagnosis and disease stage screening with ensemble learning
MH Alshayeji, SCB Sindhu - Expert Systems with Applications, 2023 - Elsevier
Diabetic retinopathy (DR), a consequence of diabetes, is among the most common causes
of vision loss. Due to a lack of symptoms in the early stages, achieving a firm diagnosis is …
of vision loss. Due to a lack of symptoms in the early stages, achieving a firm diagnosis is …
A Robust Machine Learning Model for Diabetic Retinopathy Classification
Ensemble learning is a process that belongs to the artificial intelligence (AI) field. It helps to
choose a robust machine learning (ML) model, usually used for data classification. AI has a …
choose a robust machine learning (ML) model, usually used for data classification. AI has a …
Leveraging ANFIS with Adam and PSO optimizers for Parkinson's disease
Parkinson's disease (PD) is an age-related neurodegenerative disorder characterized by
motor deficits, including tremor, rigidity, bradykinesia, and postural instability. According to …
motor deficits, including tremor, rigidity, bradykinesia, and postural instability. According to …
A hybrid semantic recommender system based on an improved clustering
P Bahrani, B Minaei-Bidgoli, H Parvin… - The Journal of …, 2024 - Springer
A recommender system is a model that automatically recommends some meaningful cases
(such as clips/films/goods/items) to the clients/people/consumers/users according to their …
(such as clips/films/goods/items) to the clients/people/consumers/users according to their …
Classification of Diabetic Retinopathy Using Ensemble of Machine Learning Classifiers with IDRiD Dataset
M Kalpana Devi, M Mary Shanthi Rani - Evolutionary Computing and …, 2022 - Springer
Diabetic retinopathy (DR) has a major impact of eye vision loss and blindness around the
world. There are several screening methods used to detect the disease. Early prevention is …
world. There are several screening methods used to detect the disease. Early prevention is …
[PDF][PDF] A Robust Machine Learning Model for Diabetic Retinopathy Classification. J
G Tabacaru, S Moldovanu, E Răducan… - Imaging, 2024 - pdfs.semanticscholar.org
Ensemble learning is a process that belongs to the artificial intelligence (AI) field. It helps to
choose a robust machine learning (ML) model, usually used for data classification. AI has a …
choose a robust machine learning (ML) model, usually used for data classification. AI has a …