Discrimination of Human Skin Burns Using Machine Learning

Abubakar, Aliyu; Ugail, Hassan

doi:10.1007/978-3-030-22871-2_43

Cited by 19 publications

(22 citation statements)

References 12 publications

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“…Burns 0 1159 The sensitivity and specificity of the classification output were calculated. Sensitivity (true positive rate) is the proportion of actual burn images that are correctly classified while specificity is the proportion of actual non burn images that are correctly classified [24]. Sensitivity is determined by…”

Section: Results From Off-the-shelf Features and Svmmentioning

confidence: 99%

“…CNN have achieved state-of-the-art recognition accuracies in many classification problems including plant disease detection [13], cancer [14,15], and skin burns assessment [8].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Study by [8] to discriminate burns and healthy skin using off-the-shelf features was proposed. The study utilised deep pre-trained neural network model to extract patterns from the given images.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In general, studies [6,7] have shown that about 265,000 deaths are reported every year, as such burns have become eminent health concern globally. Assessment of burn injury clinically by experience personnel via visual observation is very subjective and totally dependent on the experience of the personnel with recorded high accuracy of approximately 75% [8], in addition of been painful due to invasive nature (blanching). Other diagnostic means such as Laser Doppler Flowmetry (LDF) Laser Doppler Imaging (LDI) were found to be effective than clinical evaluation.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Results From Off-the-shelf Features and Svmmentioning

confidence: 99%

“…CNN have achieved state-of-the-art recognition accuracies in many classification problems including plant disease detection [13], cancer [14,15], and skin burns assessment [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Abubakar¹,

Ajuji²,

Yahya³

2020

Preprint

Self Cite

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“…Assessment of burns is effective clinically when the burn injuries involves superficial or full-thickness and requires experienced dermatologist or surgeons and time consuming [1]. Overall, the assessment of burns by experienced clinicians is accurate in only between 65-75% [8,9]. For this reason, certain number of modalities were introduced to aid clinicians in making accurate evaluation of the burn injuries.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Abubakar

Ajuji

Yahya

2020

ASI

Self Cite

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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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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 Human Skin Burns Using Machine Learning

Cited by 19 publications

References 12 publications

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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