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

Fakhri Alam Khan, Ateeq Ur Rehman Butt, Muhammad Asif*, Waqar Ahmad, Muhammad Nawaz, Mona Jamjoom, Eatedal Alabdulkreem

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


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.

Original languageEnglish
Pages (from-to)34545-34568
Number of pages24
JournalMultimedia Tools and Applications
Issue number45-46
StatePublished - Dec 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020, Springer Science+Business Media, LLC, part of Springer Nature.


  • Burnt skin images
  • CAD
  • Classification
  • DCNN
  • Feature extraction
  • Image enhancement
  • Image mining
  • Segmentation

ASJC Scopus subject areas

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications


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