Search Results for author:
Found 12 papers, 4 papers with code
Integrated Image and Location Analysis for Wound Classification: A Deep Learning Approach
The global burden of acute and chronic wounds presents a compelling case for enhancing wound classification methods, a vital step in diagnosing and determining optimal treatments.
FUSegNet: A Deep Convolutional Neural Network for Foot Ulcer Segmentation
As the top decoder stage carries a limited number of feature maps, max-out scSE is bypassed there to form a shorted P-scSE.
Wound Severity Classification using Deep Neural Network
This study used wound photos to construct a deep neural network-based wound severity classifier that classified them into one of three classes: green, yellow, or red.
Machine learning techniques to identify antibiotic resistance in patients diagnosed with various skin and soft tissue infections
We trained an individual ML algorithm on each antimicrobial family to determine whether a Gram-Positive Cocci (GPC) or Gram-Negative Bacilli (GNB) bacteria will resist the corresponding antibiotic.
FUSeg: The Foot Ulcer Segmentation Challenge
Segmentation of wound boundaries in images is a key component of the care and diagnosis protocol since it is important to estimate the area of the wound and provide quantitative measurement for the treatment.
Multi-modal Wound Classification using Wound Image and Location by Deep Neural Network
This study developed a deep neural network-based multi-modal classifier using wound images and their corresponding locations to categorize wound images into multiple classes, including diabetic, pressure, surgical, and venous ulcers.
Synthesizing time-series wound prognosis factors from electronic medical records using generative adversarial networks
We utilized samples generated by our proposed GAN in training a prognosis model to demonstrate its real-life application.
Multiclass Burn Wound Image Classification Using Deep Convolutional Neural Networks
A pre-trained deep convolutional neural network, AlexNet, is fine-tuned using a burn wound image dataset and utilized as the classifier.
Multiclass Wound Image Classification using an Ensemble Deep CNN-based Classifier
Acute and chronic wounds are a challenge to healthcare systems around the world and affect many people's lives annually.
Fully Automatic Wound Segmentation with Deep Convolutional Neural Networks
Fully automatic segmentation of wound areas in natural images is an important part of the diagnosis and care protocol since it is crucial to measure the area of the wound and provide quantitative parameters in the treatment.
Image Based Artificial Intelligence in Wound Assessment: A Systematic Review
Efficient and effective assessment of acute and chronic wounds can help wound care teams in clinical practice to greatly improve wound diagnosis, optimize treatment plans, ease the workload and achieve health related quality of life to the patient population.
A Mobile App for Wound Localization using Deep Learning
We present an automated wound localizer from 2D wound and ulcer images by using deep neural network, as the first step towards building an automated and complete wound diagnostic system.
