Lakhasly

Burns being one of the leading causes of clinically significant morbidity can lead to a dramatic physiological reaction with prolonged repercussions, metabolic disturbance, severe scarring, catastrophic organ failure, and death if not properly treated.In order to validate the efficacy of the suggested strategy, the study also applies a traditional solution to mitigate this multi-class categorization problem, incorporating rigorous digital image processing steps with several conventional machine learning classifiers and then conducts a comparative performance assessment.Thus, through the use of intelligent technologies, the proposed DCNN-based technique can aid healthcare practitioners in evaluating the burn damage condition and providing appropriate treatments in the shortest feasible time, remarkably reducing the unfavorable consequences of burns.

Burns being one of the leading causes of clinically significant morbidity can lead to a dramatic physiological reaction with prolonged repercussions, metabolic disturbance, severe scarring, catastrophic organ failure, and death if not properly treated. Appropriate burn treatment management is associated with the severity of burn wounds which can be extremely challenging to anticipate at an early stage due to various factors using traditional clinical methods. Therefore, this study proposed a Deep Convolutional Neural Network (DCNN) based approach for detecting the severity of burn injury utilizing real-time images of skin burns. The DCNN architecture leverage the utilization of transfer learning with fine tuning employing three types of pretrained models on top of multiple convolutional layers with hyperparameter tuning for feature extraction from the images and then a fully connected feed forward neural network to classify the images into three categories according to their burn severity : first, second and third degree burns. In order to validate the efficacy of the suggested strategy, the study also applies a traditional solution to mitigate this multi-class categorization problem, incorporating rigorous digital image processing steps with several conventional machine learning classifiers and then conducts a comparative performance assessment. The study’s findings demonstrate that using pretrained models, the recommended DCNN model has gained significantly greater accuracy, with the highest accuracy being obtained using the VGG16 pretrained model for transfer learning with an accuracy of 95.63% . Thus, through the use of intelligent technologies, the proposed DCNN-based technique can aid healthcare practitioners in evaluating the burn damage condition and providing appropriate treatments in the shortest feasible time, remarkably reducing the unfavorable consequences of burns.