Artificial Intelligence in Retinal Screening Using OCT Images: A Review of the Last Decade (2013-2023)
Background and objectives Optical coherence tomography (OCT) has ushered in a
transformative era in the domain of ophthalmology, offering non-invasive imaging with high …
transformative era in the domain of ophthalmology, offering non-invasive imaging with high …
Computationally efficient deep learning models for diabetic retinopathy detection: a systematic literature review
Diabetic retinopathy, often resulting from conditions like diabetes and hypertension, is a
leading cause of blindness globally. With diabetes affecting millions worldwide and …
leading cause of blindness globally. With diabetes affecting millions worldwide and …
Htc-retina: a hybrid retinal diseases classification model using transformer-convolutional neural network from optical coherence tomography images
A Laouarem, C Kara-Mohamed, EB Bourennane… - Computers in Biology …, 2024 - Elsevier
Retinal diseases are among nowadays major public health issues, deservedly needing
advanced computer-aided diagnosis. We propose a hybrid model for multi label …
advanced computer-aided diagnosis. We propose a hybrid model for multi label …
[HTML][HTML] Deep integrated fusion of local and global features for cervical cell classification
Cervical cytology image classification is of great significance to the cervical cancer
diagnosis and prognosis. Recently, convolutional neural network (CNN) and visual …
diagnosis and prognosis. Recently, convolutional neural network (CNN) and visual …
Automated retinal disease classification using hybrid transformer model (SViT) using optical coherence tomography images
GR Hemalakshmi, M Murugappan… - Neural Computing and …, 2024 - Springer
Optical coherence tomography (OCT) is a widely used imaging technique in ophthalmology
for diagnosis and treatment. Recent advances in deep neural networks (DNNs) and vision …
for diagnosis and treatment. Recent advances in deep neural networks (DNNs) and vision …
Comparison of Different Methods for Building Ensembles of Convolutional Neural Networks
In computer vision and image analysis, Convolutional Neural Networks (CNNs) and other
deep-learning models are at the forefront of research and development. These advanced …
deep-learning models are at the forefront of research and development. These advanced …
Laryngeal Cancer Detection and Classification Using Aquila Optimization Algorithm with Deep Learning on Throat Region Images
Laryngeal cancer detection on throat area images is a vital application of medical image
diagnosis and computer vision (CV) in the healthcare domain. It contains the analysis and …
diagnosis and computer vision (CV) in the healthcare domain. It contains the analysis and …
Deep local-to-global feature learning for medical image super-resolution
Medical images play a vital role in medical analysis by providing crucial information about
patients' pathological conditions. However, the quality of these images can be compromised …
patients' pathological conditions. However, the quality of these images can be compromised …
[HTML][HTML] Discriminative, generative artificial intelligence, and foundation models in retina imaging
P Ruamviboonsuk, N Arjkongharn… - Taiwan Journal of …, 2024 - journals.lww.com
Recent advances of artificial intelligence (AI) in retinal imaging found its application in two
major categories: discriminative and generative AI. For discriminative tasks, conventional …
major categories: discriminative and generative AI. For discriminative tasks, conventional …
Stitched vision transformer for age-related macular degeneration detection using retinal optical coherence tomography images
Age-related macular degeneration (AMD) is an eye disease that leads to the deterioration of
the central vision area of the eye and can gradually result in vision loss in elderly …
the central vision area of the eye and can gradually result in vision loss in elderly …