Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda
Artificial intelligence can assist providers in a variety of patient care and intelligent health
systems. Artificial intelligence techniques ranging from machine learning to deep learning …
systems. Artificial intelligence techniques ranging from machine learning to deep learning …
Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends
Semantic-based segmentation (Semseg) methods play an essential part in medical imaging
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …
Artificial intelligence-based mitosis detection in breast cancer histopathology images using faster R-CNN and deep CNNs
Breast cancer is the leading cause of mortality in women. Early diagnosis of breast cancer
can reduce the mortality rate. In the diagnosis, the mitotic cell count is an important …
can reduce the mortality rate. In the diagnosis, the mitotic cell count is an important …
Modified U-Net architecture for semantic segmentation of diabetic retinopathy images
Segmentation of lesions from fundus images is an essential prerequisite for accurate
severity assessment of diabetic retinopathy. Due to variation in morphologies, number and …
severity assessment of diabetic retinopathy. Due to variation in morphologies, number and …
[HTML][HTML] Artificial Intelligence-based computer-aided diagnosis of glaucoma using retinal fundus images
Glaucoma is one of the most common chronic diseases that may lead to irreversible vision
loss. The number of patients with permanent vision loss due to glaucoma is expected to …
loss. The number of patients with permanent vision loss due to glaucoma is expected to …
T-Net: A resource-constrained tiny convolutional neural network for medical image segmentation
TM Khan, A Robles-Kelly… - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
In this paper, we present T-Net, a fully convolutional net-work particularly well suited for
resource constrained andmobile devices, which cannot cater for the computationalresources …
resource constrained andmobile devices, which cannot cater for the computationalresources …
Retinal vessel segmentation via a Multi-resolution Contextual Network and adversarial learning
Timely and affordable computer-aided diagnosis of retinal diseases is pivotal in precluding
blindness. Accurate retinal vessel segmentation plays an important role in disease …
blindness. Accurate retinal vessel segmentation plays an important role in disease …
Artificial intelligence: the unstoppable revolution in ophthalmology
D Benet, OJ Pellicer-Valero - Survey of ophthalmology, 2022 - Elsevier
Artificial intelligence (AI) is an unstoppable force that is starting to permeate all aspects of
our society as part of the revolution being brought into our lives (and into medicine) by the …
our society as part of the revolution being brought into our lives (and into medicine) by the …
Detecting retinal vasculature as a key biomarker for deep Learning-based intelligent screening and analysis of diabetic and hypertensive retinopathy
Retinal vessels are considered important biomarkers for the detection of retinal diseases,
like diabetic retinopathy (caused by diabetes) and hypertensive retinopathy (caused by …
like diabetic retinopathy (caused by diabetes) and hypertensive retinopathy (caused by …
G-net light: a lightweight modified google net for retinal vessel segmentation
In recent years, convolutional neural network architectures have become increasingly
complex to achieve improved performance on well-known benchmark datasets. In this …
complex to achieve improved performance on well-known benchmark datasets. In this …