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Computer-aided diagnosis for burnt skin images using deep convolutional neural network

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A Correction to this article was published on 10 August 2022

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Abstract

Numerous patients died every year due to the leading causes of deaths all over the world and burn injuries are one of them. Burn injury cases are most viewed in low and middle-income countries (LMIC). Researchers show great interest to classify the burn into different depths through digital means. In Pakistan, at provisional level, it’s really a significant issue to categorize the burn and its depths due to the non-availability of expert doctors and surgeons; hence the decision for the correct first treatment can't be made, so this may cause a serious issue later on. The main objectives of this research work are to segment the burn wounds and classification of burn depths into 1st, 2nd and 3rd degrees respectively. A real-time dataset of burnt patients has been collected from the burn unit of Allied Hospital Faisalabad, Pakistan. The dataset used for this research task contains 450 images of all the three levels of burn depths. Segmentation of the burnt area was done by the use of Otsu's method of thresholding and feature vector was obtained through the use of statistical methods. We have used the Deep Convolutional Neural Network (DCNN) to estimate the burn depths. The network was trained by 65 percent of the images and the remaining 35 percent images were used for testing the accuracy of the classifier. The maximum average accuracy obtained by using the Deep Convolutional Neural Network (DCNN) classifier is reported round about 79.4% and these results are the best if we compare them with previous results. From the obtained results of this research work, non-expert doctors will be able to apply the correct first treatment for the quality evaluation of burn depths.

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Acknowledgments

The authors would like to thank Dr. Saeed Ashraf Cheema, Professor, Faisalabad Medical College, and Department of Plastic Surgery for the useful technical comments.

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Authors and Affiliations

  1. Center of Excellence in Data Science (CEDS-IMSciences), Institute of Management Sciences, Peshawar, Pakistan

    Fakhri Alam Khan

  2. National Textile University, Faisalabad, Punjab, Pakistan

    Ateeq Ur Rehman Butt, Muhammad Asif & Waqar Ahmad

  3. Institute of Management Sciences, Peshawar, Pakistan

    Muhammad Nawaz

  4. Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia

    Mona Jamjoom & Eatedal Alabdulkreem

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  1. Fakhri Alam Khan
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  2. Ateeq Ur Rehman Butt
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  3. Muhammad Asif
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  4. Waqar Ahmad
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  5. Muhammad Nawaz
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Correspondence to Muhammad Asif.

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The original online version of this article was revised: The original publication of this article contains incorrect spelling of the university name of the fourth affiliation. "Nora" should be spelled as "Nourah". The original article has been corrected.

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Khan, F.A., Butt, A.U.R., Asif, M. et al. Computer-aided diagnosis for burnt skin images using deep convolutional neural network. Multimed Tools Appl 79, 34545–34568 (2020). https://doi.org/10.1007/s11042-020-08768-y

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