Spatial attention-based residual network for human burn identification and classification

Abstract

Diagnosing burns in humans has become critical, as early identification can save lives. The manual process of burn diagnosis is time-consuming and complex, even for experienced doctors. Machine learning (ML) and deep convolutional neural network (CNN) models have emerged as the standard for medical image diagnosis. The ML-based approach typically requires handcrafted features for training, which may result in suboptimal performance. Conversely, DL-based methods automatically extract features, but designing a robust model is challenging. Additionally, shallow DL methods lack long-range feature dependency, decreasing efficiency in various applications. We implemented several deep CNN models, ResNeXt, VGG16, and AlexNet, for human burn diagnosis. The results obtained from these models were found to be less reliable since shallow deep CNN models need improved attention modules to preserve the feature dependencies. Therefore, in the proposed study, the feature map is divided into several categories, and the channel dependencies between any two channel mappings within a given class are highlighted. A spatial attention map is built by considering the links between features and their locations. Our attention-based model BuRnGANeXt50 kernel and convolutional layers are also optimized for human burn diagnosis. The earlier study classified the burn based on depth of graft and non-graft. We first classified the burn based on the degree. Subsequently, it is classified into graft and non-graft. Furthermore, the proposed model performance is evaluated on Burns_BIP_US_database. The sensitivity of the BuRnGANeXt50 is 97.22% and 99.14%, respectively, for classifying burns based on degree and depth. This model may be used for quick screening of burn patients and can be executed in the cloud or on a local machine. The code of the proposed method can be accessed at https://github.com/dhirujis02/Journal.git for the sake of reproducibility.

Review of machine learning for optical imaging of burn wound severity assessment

“…Although some preliminary burn classification work using digital color images and deep learning technology had been reported prior to 2019, 27 the period from 2019 to 2023 saw a substantial increase in the use of deep learning approaches for burn wound classification. [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45] Several studies in this time period used deep learning algorithms to segment images into burned and un-burned regions. 31,34,35,[38][39][40] A 2019 study 31 used 1,000 images to train a mask region with a convolutional neural network (Mask R-CNN) algorithm, comparing several different underlying network types and obtaining a maximum accuracy of 85% for identifying burn regions in images of different severities of burns.…”

“…Despite the high AUC, the overall accuracy of the algorithm was 64.5%. Two studies by the same group 43 , 44 used new deep CNN algorithms to classify burn severity (superficial, deep dermal, and full thickness) with >97% accuracy and distinguish between burns in need of grafting and burns not requiring grafts with >99% accuracy, using fivefold cross-validation.…”

“…Although some preliminary burn classification work using digital color images and deep learning technology had been reported prior to 2019, 27 the period from 2019 to 2023 saw a substantial increase in the use of deep learning approaches for burn wound classification. 28 – 45 …”