Related references
Note: Only part of the references are listed.RetoNet: a deep learning architecture for automated retinal ailment detection
Lekha R. Nair
MULTIMEDIA TOOLS AND APPLICATIONS (2020)
Deep learning approach to detect malaria from microscopic images
A. Vijayalakshmi et al.
MULTIMEDIA TOOLS AND APPLICATIONS (2020)
DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification
Manu Goyal et al.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE (2020)
Deep convolutional representations and kernel extreme learning machines for image classification
Xiaobin Zhu et al.
MULTIMEDIA TOOLS AND APPLICATIONS (2019)
Sketch recognition using transfer learning
Mustafa Sert et al.
MULTIMEDIA TOOLS AND APPLICATIONS (2019)
Deep learning based feature representation for automated skin histopathological image annotation
Gang Zhang et al.
MULTIMEDIA TOOLS AND APPLICATIONS (2018)
FDCNet: filtering deep convolutional network for marine organism classification
Huimin Lu et al.
MULTIMEDIA TOOLS AND APPLICATIONS (2018)
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification
Justin Salamon et al.
IEEE SIGNAL PROCESSING LETTERS (2017)
A survey on deep learning in medical image analysis
Geert Litjens et al.
MEDICAL IMAGE ANALYSIS (2017)
Age classification with deep learning face representation
Jin Huang et al.
MULTIMEDIA TOOLS AND APPLICATIONS (2017)
Deep Transfer Learning for Modality Classification of Medical Images
Yuhai Yu et al.
INFORMATION (2017)
Diabetic foot ulcer mobile detection system using smart phone thermal camera: a feasibility study
Luay Fraiwan et al.
BIOMEDICAL ENGINEERING ONLINE (2017)
Densely Connected Convolutional Networks
Gao Huang et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)
Area Determination of Diabetic Foot Ulcer Images Using a Cascaded Two-Stage SVM-Based Classification
Lei Wang et al.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2016)
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky et al.
INTERNATIONAL JOURNAL OF COMPUTER VISION (2015)
Automatic detection of diabetic foot complications with infrared thermography by asymmetric analysis
Chanjuan Liu et al.
JOURNAL OF BIOMEDICAL OPTICS (2015)
Wound image evaluation with machine learning
Francisco J. Veredas et al.
NEUROCOMPUTING (2015)
Automatic Analysis of Plantar Foot Thermal Images in at-Risk Type II Diabetes by Using an Infrared Camera
L. Vilcahuaman et al.
WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING, 2015, VOLS 1 AND 2 (2015)
Assessment of Signs of Foot Infection in Diabetes Patients Using Photographic Foot Imaging and Infrared Thermography
Constantijn E. V. B. Hazenberg et al.
DIABETES TECHNOLOGY & THERAPEUTICS (2014)
A systematic review of IT for diabetes self-management: Are we there yet?
Omar El-Gayar et al.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS (2013)
Cost of treating diabetic foot ulcers in five different countries
Peter Cavanagh et al.
DIABETES-METABOLISM RESEARCH AND REVIEWS (2012)
Telemedicine: What more is needed for its integration in everyday life?
S. Franc et al.
DIABETES & METABOLISM (2011)
Remote assessment of diabetic foot ulcers using a novel wound imaging system
Frank L. Bowling et al.
WOUND REPAIR AND REGENERATION (2011)
Binary Tissue Classification on Wound Images With Neural Networks and Bayesian Classifiers
Francisco Veredas et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2010)
The global burden of diabetic foot disease
AJM Boulton et al.
LANCET (2005)
Global prevalence of diabetes - Estimates for the year 2000 and projections for 2030
S Wild et al.
DIABETES CARE (2004)
Unsupervised segmentation of color-texture regions in images and video
YN Deng et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2001)
Share Paper
