Discrimination of Healthy Skin, Superficial Epidermal Burns, and Full-Thickness Burns from 2D-Colored Images Using Machine Learning

Abubakar, Aliyu; Ugail, Hassan; Bukar, Ali Maina; Smith, Kirsty

doi:10.1201/9780429263798-9

Cited by 6 publications

(5 citation statements)

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“…Skin burns are categorized into classes depending on whether the injury affects the top-most layer, intermediate or deep layer. In this regard, the authors of Reference [16] proposed a study to classify healthy skin, superficial and deep dermal (full-thickness) burns using pre-trained deep learning model and SVM. Specifically, they used ResNet101 for the extraction of image feature extraction and SVM for the classification task, in which identification accuracy of 99.9% was achieved using 10-folds cross-validation strategy.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Four pre-trained deep learning models were used to classify the burn images transfer learning approach, in which earlier convolution layers were frozen to extract features and the top-most layers were modified and trained using features obtained from the frozen layers. This process was applied to several deep neural network models including VGG- 16…”

Section: Literature Reviewmentioning

confidence: 99%

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Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Abubakar

Ajuji

Yahya

2020

ASI

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While visual assessment is the standard technique for burn evaluation, computer-aided diagnosis is increasingly sought due to high number of incidences globally. Patients are increasingly facing challenges which are not limited to shortage of experienced clinicians, lack of accessibility to healthcare facilities and high diagnostic cost. Certain number of studies were proposed in discriminating burn and healthy skin using machine learning leaving a huge and important gap unaddressed; whether burns and related skin injuries can be effectively discriminated using machine learning techniques. Therefore, we specifically use transfer learning by leveraging pre-trained deep learning models due to deficient dataset in this paper, to discriminate two classes of skin injuries—burnt skin and injured skin. Experiments were extensively conducted using three state-of-the-art pre-trained deep learning models that includes ResNet50, ResNet101 and ResNet152 for image patterns extraction via two transfer learning strategies—fine-tuning approach where dense and classification layers were modified and trained with features extracted by base layers and in the second approach support vector machine (SVM) was used to replace top-layers of the pre-trained models, trained using off-the-shelf features from the base layers. Our proposed approach records near perfect classification accuracy in categorizing burnt skin ad injured skin of approximately 99.9%.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Abubakar

Ajuji

Yahya

2020

ASI

Self Cite

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show abstract

“…Skin burns are categorised into classes depending on whether the injury affect top-most layer, intermediate or deep layer. In this regard, authors in [16] proposed a study to classify healthy skin, superficial and deep dermal (full-thickness) burns using pre-trained deep learning model and SVM.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Abubakar¹,

Ajuji²,

Yahya³

2020

Preprint

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While visual assessment is the standard technique for burn evaluation, computer-aided diagnosis is increasingly sought due to high number of incidences globally. Patients are increasingly facing challenges which are not limited to shortage of experienced clinicians, lack of accessibility to healthcare facilities, and high diagnostic cost. Certain number of studies were proposed in discriminating burn and healthy skin using machine learning leaving a huge and important gap unaddressed; whether burns and related skin injuries can be effectively discriminated using machine learning techniques. Therefore, we specifically use pre-trained deep learning models due to deficient dataset to train a new model from scratch. Experiments were extensively conducted using three stateof-the-art pre-trained deep learning models that includes ResNet50, ResNet101 and ResNet152 for image patterns extraction via two transfer learning strategies: fine-tuning approach where dense and classification layers were modified and trained with features extracted by base layers, and in the second approach support vector machine (SVM) was used to replace top-layers of the pre-trained models, trained using off-the-shelf features from the base layers. Our proposed approach records near perfect classification accuracy of approximately 99.9%.

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“…We believe the research is not satisfactory without taking ethnic or racial representations during the training process. More than 90% of the epidemiology of burn related injuries occur in low/middle-income countries such as those in African and Asian counties [31]. Therefore, we further explore whether a trained model on a specific ethnic dataset can provide good identification accuracy on other racial datasets.…”

Section: Resultsmentioning

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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Discrimination of Healthy Skin, Superficial Epidermal Burns, and Full-Thickness Burns from 2D-Colored Images Using Machine Learning

Cited by 6 publications

References 1 publication

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Assessment of Human Skin Burns: A Deep Transfer Learning Approach

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