Table 2.

Summary of deep learning studies on wound tissue segmentation and classification

Works	Goals	Methods	Database	Results
Sofia Zahia [2018, USA] Tissue classification and segmentation of pressure injuries using ConvNets57	Segmentation of the different tissue types present in pressure injuries (granulation, slough, and necrotic tissues) using a small database	ConvNet (5 × 5 inputs)	22 images 1,020 × 1,020 Patches 5 × 5 -75% for training set and 25% for test set	Accuracy = 92.01% DSC = 91.38% Precision per class: Granulation = 97.31% Necrotic = 96.59% Slough = 77.90%
H. Nejati [2018, Singapore] Fine-grained wound tissue analysis using deep neural network61	Classification of seven types of tissues (necrotic, slough, infected, epithelialization, healthy, unhealthy, hyper granulation)	AlexNet (227 × 227 inputs) SVM (HSV, LBP, HSV+LBP)—Principal component analysis	350 images Patches 20 × 20 Resizing patches to 227 × 227	Three-fold cross validation: AlexNet = 86.40% HSV = 77.57% LBP = 79.66% HSV+LBP = 77.09%
Fangzhao Li [2018, China] A composite model of wound segmentation based on traditional methods and deep neural networks63	Wound image segmentation framework that combines traditional digital image processing and deep learning methods	FCN (MobileNet)	950 images	Precision = 94;69%
Manu Goyal [2017, UK] DFUNet: CNNs for DFU classification84	Novel fast CNN architecture called DFUNet for classification of ulcers and non-ulcerous skin which outperformed GoogLeNet and AlexNets	DFUNet LeNet AlexNet GoogleNet SVM (LBP) SVM (LBP+HOG) SVM (LBP+HOG+color descriptors)	292 images of patient's foot with ulcer and 105 images of the healthy foot Patches 256 × 256 -85% for training set, 5% for validation set and 10% testing set Data Augmentation (rotation, flipping, color spaces)	AUC curve: DFUNet = 0.9608 LeNet = 0.9292 AlexNet = 0.9504 GoogleNet = 0.9604 LBP = 0.9322 LBP+HOG = 0.9308 LBP+HOG+Color Descriptors = 0.9430
Manu Goyal [2017, UK] Fully convolutional networks for diabetic foot ulcer segmentation58	Automated segmentation of DFU and its surrounding skin by using fully connected networks	FCN-AlexNet FCN-32s FCN-16s FCN-8s	600 DFU images and 105 healthy foot images From 600 DFU images in the dataset, they produced 600 ROIs of DFU and 600 ROIs for surrounding skin around the DFU.	Specificity for Ulcer: FCN-AlexNet = 0.982 FCN-32s = 0.986 FCN-16s = 0.986 FCN-8s = 0.987 Specificity for Surrounding skin: FCN-AlexNet = 0.991 FCN-32s = 0.989 FCN-16s = 0.994 FCN-8s = 0.993
Changhan Wang [2015, USA] A unified framework for automatic wound segmentation and analysis with CNN62	Wound segmentation for surface area estimation and features extraction Infection detection Healing progress prediction	ConvNet Kernel SVM Gaussian Process Regression	350 images Patches 20 × 20 Resizing patches to 227 × 227	Accuracy: SVM (RGB) = 77.6% ConvNet = 95%

Wound Imaging: ready for smart assessment and monitoring.

AUC, area under the curve; CNNs, convolutional neural networks; DFU, diabetic foot ulcer; DSC, dice similarity coefficient; FCN, fully convolutional network; HOG, histogram of oriented gradients; HSV, hue saturated value; LBP, local binary pattern; SVM, support vector machine.