A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks

Li, Fangzhao; Wang, Changjian; Liu, Xiaohui; Peng, Yuxing; Jin, Shiyao

doi:10.1155/2018/4149103

Cited by 64 publications

(49 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Extensive experiments were conducted to investigate the effectiveness of our network. FCN-VGG-16 is a popular network architecture for wound image segmentation 17 , 27 . Thus, we trained this network on our dataset as the baseline model.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Fully automatic wound segmentation with deep convolutional neural networks

Wang

Anisuzzaman

Williamson

et al. 2020

Sci Rep

164

View full text Add to dashboard Cite

Acute and chronic wounds have varying etiologies and are an economic burden to healthcare systems around the world. The advanced wound care market is expected to exceed $22 billion by 2024. Wound care professionals rely heavily on images and image documentation for proper diagnosis and treatment. Unfortunately lack of expertise can lead to improper diagnosis of wound etiology and inaccurate wound management and documentation. 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. Various deep learning models have gained success in image analysis including semantic segmentation. This manuscript proposes a novel convolutional framework based on MobileNetV2 and connected component labelling to segment wound regions from natural images. The advantage of this model is its lightweight and less compute-intensive architecture. The performance is not compromised and is comparable to deeper neural networks. We build an annotated wound image dataset consisting of 1109 foot ulcer images from 889 patients to train and test the deep learning models. We demonstrate the effectiveness and mobility of our method by conducting comprehensive experiments and analyses on various segmentation neural networks. The full implementation is available at https://github.com/uwm-bigdata/wound-segmentation.

show abstract

Section: Resultsmentioning

confidence: 99%

“…To evaluate the performance of the proposed method, we compared the segmentation results achieved by our methods with those by FCN-VGG-16 17 , 27 , SegNet 16 , and Mask-RCNN 33 , 34 . We also added 2D U-Net 35 to the comparison due to its outstanding segmentation performance on biomedical images with a relatively small training dataset.…”

Section: Resultsmentioning

confidence: 99%

Fully automatic wound segmentation with deep convolutional neural networks

Wang

Anisuzzaman

Williamson

et al. 2020

Sci Rep

164

View full text Add to dashboard Cite

show abstract

“…on the images in your training set to effectively increase the number of training images [19]. Another approach is to highlight features of interest before passing the data to a CNN with CV-based methods such as background subtraction and segmentation [39].…”

Section: Overcoming the Challenges Of Deep Learningmentioning

confidence: 99%

Deep Learning vs. Traditional Computer Vision

Mahony

Campbell

Carvalho

et al. 2019

Advances in Intelligent Systems and Computing

708

383

View full text Add to dashboard Cite

Deep Learning has pushed the limits of what was possible in the domain of Digital Image Processing. However, that is not to say that the traditional computer vision techniques which had been undergoing progressive development in years prior to the rise of DL have become obsolete. This paper will analyse the benefits and drawbacks of each approach. The aim of this paper is to promote a discussion on whether knowledge of classical computer vision techniques should be maintained. The paper will also explore how the two sides of computer vision can be combined. Several recent hybrid methodologies are reviewed which have demonstrated the ability to improve computer vision performance and to tackle problems not suited to Deep Learning. For example, combining traditional computer vision techniques with Deep Learning has been popular in emerging domains such as Panoramic Vision and 3D vision for which Deep Learning models have not yet been fully optimised.

show abstract

“…Deep convolutional neural network (CNN) has achieved significant success in the field of computer vision, such as image classification [1], target tracking [2], target detection [3], and semantic image segmentation [4,5]. For example, in the ImageNet Large Scale Visual Recognition Challenge 2012 (ILSVRC2012), Krizhevsky et al won the championship with an AlexNet [1] model of about 60 million parameters and eight layers.…”

Section: Introductionmentioning

confidence: 99%

A Novel Image Classification Approach via Dense-MobileNet Models

Wang

Zou

et al. 2020

Mobile Information Systems

191

102

View full text Add to dashboard Cite

As a lightweight deep neural network, MobileNet has fewer parameters and higher classification accuracy. In order to further reduce the number of network parameters and improve the classification accuracy, dense blocks that are proposed in DenseNets are introduced into MobileNet. In Dense-MobileNet models, convolution layers with the same size of input feature maps in MobileNet models are taken as dense blocks, and dense connections are carried out within the dense blocks. e new network structure can make full use of the output feature maps generated by the previous convolution layers in dense blocks, so as to generate a large number of feature maps with fewer convolution cores and repeatedly use the features. By setting a small growth rate, the network further reduces the parameters and the computation cost. Two Dense-MobileNet models, Dense1-MobileNet and Dense2-MobileNet, are designed. Experiments show that Dense2-MobileNet can achieve higher recognition accuracy than MobileNet, while only with fewer parameters and computation cost.

show abstract

A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks

Cited by 64 publications

References 16 publications

Fully automatic wound segmentation with deep convolutional neural networks

Fully automatic wound segmentation with deep convolutional neural networks

Deep Learning vs. Traditional Computer Vision

A Novel Image Classification Approach via Dense-MobileNet Models

Contact Info

Product

Resources

About