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Exudates and Transudates

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[Diagnosis of malignant pleural effusions using convolutional neural networks by the morphometric image analysis of facies of pleural exudate].

Khirurgiia
OBJECTIVE: To estimate the possibility of diagnosis of malignant pleural effusion using convolutional neural networks of facies images of pleural exudates obtained by the method of wedge-shaped dehydration.

Systematic Comparison of Heatmapping Techniques in Deep Learning in the Context of Diabetic Retinopathy Lesion Detection.

Translational vision science & technology
PURPOSE: Heatmapping techniques can support explainability of deep learning (DL) predictions in medical image analysis. However, individual techniques have been mainly applied in a descriptive way without an objective and systematic evaluation. We in...

EAD-Net: A Novel Lesion Segmentation Method in Diabetic Retinopathy Using Neural Networks.

Disease markers
Diabetic retinopathy (DR) is a common chronic fundus disease, which has four different kinds of microvessel structure and microvascular lesions: microaneurysms (MAs), hemorrhages (HEs), hard exudates, and soft exudates. Accurate detection and countin...

Automated detection of retinal exudates and drusen in ultra-widefield fundus images based on deep learning.

Eye (London, England)
BACKGROUND: Retinal exudates and/or drusen (RED) can be signs of many fundus diseases that can lead to irreversible vision loss. Early detection and treatment of these diseases are critical for improving vision prognosis. However, manual RED screenin...

Deep CNN with Hybrid Binary Local Search and Particle Swarm Optimizer for Exudates Classification from Fundus Images.

Journal of digital imaging
Diabetic retinopathy is a chronic condition that causes vision loss if not detected early. In the early stage, it can be diagnosed with the aid of exudates which are called lesions. However, it is arduous to detect the exudate lesion due to the avail...

LightEyes: A Lightweight Fundus Segmentation Network for Mobile Edge Computing.

Sensors (Basel, Switzerland)
Fundus is the only structure that can be observed without trauma to the human body. By analyzing color fundus images, the diagnosis basis for various diseases can be obtained. Recently, fundus image segmentation has witnessed vast progress with the d...

Classification of pleural effusions using deep learning visual models: contrastive-loss.

Scientific reports
Blood and fluid analysis is extensively used for classifying the etiology of pleural effusion. However, most studies focused on determining the presence of a disease. This study classified pleural effusion etiology employing deep learning models by a...

Artificial intelligence for detection of effusion and lipo-hemarthrosis in X-rays and CT of the knee.

European journal of radiology
BACKGROUND: Traumatic knee injuries are challenging to diagnose accurately through radiography and to a lesser extent, through CT, with fractures sometimes overlooked. Ancillary signs like joint effusion or lipo-hemarthrosis are indicative of fractur...

A deep learning approach to hard exudates detection and disorganization of retinal inner layers identification on OCT images.

Scientific reports
The purpose of the study was to detect Hard Exudates (HE) and classify Disorganization of Retinal Inner Layers (DRIL) implementing a Deep Learning (DL) system on optical coherence tomography (OCT) images of eyes with diabetic macular edema (DME). We ...

Neural Network-Enhanced Electrochemical/SERS Dual-Mode Microfluidic Platform for Accurate Detection of Interleukin-6 in Diabetic Wound Exudates.

Analytical chemistry
Interleukin-6 (IL-6) plays a pivotal role in the inflammatory response of diabetic wounds, providing critical insights for clinicians in the development of personalized treatment strategies. However, the low concentration of IL-6 in biological sample...