Novel CNN-Based Approach for Burn Severity Assessment and Fine-Grained Boundary Segmentation in Burn Images
Mahla Abdolahnejad, Justin J. Lee, Hannah Chan, Alexander Morzycki, Olivier Ethier, Anthea Mo, Peter Liu, Joshua N. Wong, Collin Hong, Rakesh Joshi
Abstract
Burn injuries, resulting from thermal, chemical, and electrical mechanisms, require prompt and accurate assessment for effective treatment. The primary method, relying on visual and tactile evaluations, offers 50%–80% accuracy, while noninvasive methods such as laser Doppler imaging (LDI) reach up to 97% accuracy. This article presents a machine learning (ML) pipeline for assessing burn severity and segmenting affected skin regions. We trained a convolutional neural network (CNN) to classify four burn severities: superficial (SPF), superficial partial thickness (SPT), deep partial thickness (DPT), and full thickness (FT). In addition, we introduced boundary attention mapping (BAM), a saliency mapping method that leverages the trained CNN to accurately segment burn regions. Our pipeline was validated using two datasets: a Burn Injury Image dataset with 1385 images and an LDI dataset with 184 images. The CNN achieved 80% accuracy, a 79.5% average F1-score, and 95% ROC in classifying burn severities. Comparing BAM with LDI, our method achieved 91.39% accuracy, 78.12% sensitivity, and 95.07% specificity in segmenting burn regions. These findings demonstrate the robustness of our AI model and its potential clinical application.