Feature Extraction Based Machine Learning for Human Burn Diagnosis From Burn Images

Yadav, Dhirendra Prasad; Sharma, Ashish; Singh, Madhusudan; Goyal, Ayush

doi:10.1109/jtehm.2019.2923628

Cited by 93 publications

(50 citation statements)

References 17 publications

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“…Studies in the literature used very deficient databases, the authors in [21] reported 73.2% accuracy on datasets of 164 images and all images were in L*a*b* colour space with the application of PCA for dimensionality reduction on texture features. Study in [22] reported overall accuracy of 82.43% using 74 images in L*a*b* colour space, another study in [23] reported a discriminatory accuracy of 79.4% using colour and texture features and DCNN as a classifier on a database of 450 images. In this proposed study, we used 1560 RGB burn images along with 520 healthy skin images thereby achieving stateof-the-art discriminatory accuracy of 95.43%.…”

Section: Discussion Of Resultsmentioning

confidence: 99%

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Burns Depth Assessment Using Deep Learning Features

et al. 2020

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Purpose Burns depth evaluation is a lifesaving task and very challenging that requires objective techniques to accomplish. While the visual assessment is the most commonly used by surgeons, its accuracy reliability ranges between 60 and 80% and subjective that lacks any standard guideline. Currently, the only standard adjunct to clinical evaluation of burn depth is Laser Doppler Imaging (LDI) which measures microcirculation within the dermal tissue, providing the burns potential healing time which correspond to the depth of the injury achieving up to 100% accuracy. However, the use of LDI is limited due to many factors including high affordability and diagnostic costs, its accuracy is affected by movement which makes it difficult to assess paediatric patients, high level of human expertise is required to operate the device, and 100% accuracy possible after 72 h. These shortfalls necessitate the need for objective and affordable technique. Method In this study, we leverage the use of deep transfer learning technique using two pretrained models ResNet50 and VGG16 for the extraction of image patterns (ResFeat50 and VggFeat16) from a a burn dataset of 2080 RGB images which composed of healthy skin, first degree, second degree and third-degree burns evenly distributed. We then use One-versus-One Support Vector Machines (SVM) for multi-class prediction and was trained using 10-folds cross validation to achieve optimum trade-off between bias and variance. Results The proposed approach yields maximum prediction accuracy of 95.43% using ResFeat50 and 85.67% using VggFeat16. The average recall, precision and F1-score are 95.50%, 95.50%, 95.50% and 85.75%, 86.25%, 85.75% for both ResFeat50 and VggFeat16 respectively. Conclusion The proposed pipeline achieved a state-of-the-art prediction accuracy and interestingly indicates that decision can be made in less than a minute whether the injury requires surgical intervention such as skin grafting or not.

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Section: Discussion Of Resultsmentioning

confidence: 99%

“…Discriminating burns depth using machine learning was also reported in a study by [22]. The aim is to provide a reliable diagnostic technique to deduce whether a sustained burn injury requires surgical intervention or not because early determination of right treatment choice can shorten the healing time.…”

Section: Literaturementioning

confidence: 94%

Burns Depth Assessment Using Deep Learning Features

et al. 2020

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“…Convolutional neural networks (CNNs) have been used in the past for burn classification. In one study, CNNs were used to produce feature maps that were fed to a support vector machine (SVM) for classification and an accuracy of 82.43% was achieved [8]. Others have also experimented with CNN architectures for burn degree determination.…”

Section: Previous Deep Learning Researchmentioning

confidence: 99%

BurnNet: An Efficient Deep Learning Framework for Accurate Dermal Burn Classification

Rm¹,

Kumar²

2021

Preprint

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Burns are the fourth most prevalent unintentional injury around the world, and when left untreated can become permanent and sometimes fatal. An important aspect of treating burn injuries is accurate and efficient diagnosis. Classifying the three primary types of burns-superficial dermal, deep dermal, and full thickness-is essential in determining the necessity of surgery, which is often critical to the afflicted patient's survival. Unfortunately, reconstructive burn surgeons and dermatologists are merely able to diagnose these types of burns with approximately 50-75% accuracy. As a result, we propose the use of an eight-layer convolutional neural network, BurnNet, for rapid and precise burn classification with 99.87% accuracy. We applied affine transformations to artificially augment our dataset and found that our model attained near perfect metrics across the board, demonstrating the high propensity of deep learning architectures in burn classification.

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“…Another study by the authors in [22] used 74 burn images in LAB colour space from Caucasian patients to discriminate burns using machine learning. The approach used handcrafted features to train a SVM which achieved a classification accuracy of 82.43%.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessment of Human Skin Burns: A Deep Transfer Learning Approach

2020

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Purpose Accurate assessment of burns is increasingly sought due to diagnostic challenges faced with traditional visual assessment methods. While visual assessment is the most established means of evaluating burns globally, specialised dermatologists are not readily available in most locations and assessment is highly subjective. The use of other technical devices such as Laser Doppler Imaging is highly expensive while rate of occurrences is high in low-and middle-income countries. These necessitate the need for robust and cost-effective assessment techniques thereby acting as an affordable alternative to human expertise. Method In this paper, we present a technique to discriminate skin burns using deep transfer learning. This is due to deficient datasets to train a model from scratch, in which two dense and a classification layers were added to replace the existing top layers of pre-trained ResNet50 model. Results The proposed study was able to discriminate between burns and healthy skin in both ethnic subjects (Caucasians and Africans). We present an extensive analysis of the effect of using both homogeneous and heterogeneous datasets when training a machine learning algorithm. The findings show that using homogenous dataset during training process produces a biased diagnostic model towards minor racial subjects while using heterogeneous datasets produce a robust diagnostic model. Recognition accuracy of up to 97.1% and 99.3% using African and Caucasian datasets respectively were achieved. Conclusion We concluded that it is feasible to have a robust diagnostic machine learning model for burns assessment that can be deployed to remote locations faced with access to specialized burns specialists, thereby aiding in decision-making as quick as possible

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Feature Extraction Based Machine Learning for Human Burn Diagnosis From Burn Images

Cited by 93 publications

References 17 publications

Burns Depth Assessment Using Deep Learning Features

Burns Depth Assessment Using Deep Learning Features

BurnNet: An Efficient Deep Learning Framework for Accurate Dermal Burn Classification

Assessment of Human Skin Burns: A Deep Transfer Learning Approach

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