BurnNet: An Efficient Deep Learning Framework for Accurate Dermal Burn Classification

Abstract

Burns are the fourth most prevalent unintentional injury around the world, and when left untreated can become permanent and sometimes fatal. An important aspect of treating burn injuries is accurate and efficient diagnosis. Classifying the three primary types of burns-superficial dermal, deep dermal, and full thickness-is essential in determining the necessity of surgery, which is often critical to the afflicted patient's survival. Unfortunately, reconstructive burn surgeons and dermatologists are merely able to diagnose these types of burns with approximately 50-75% accuracy. As a result, we propose the use of an eight-layer convolutional neural network, BurnNet, for rapid and precise burn classification with 99.87% accuracy. We applied affine transformations to artificially augment our dataset and found that our model attained near perfect metrics across the board, demonstrating the high propensity of deep learning architectures in burn classification.

A Survey of Wound Image Analysis Using Deep Learning: Classification, Detection, and Segmentation

“…Medetec dataset [24] Mixed -No [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35] BIP_US database [36] Burn 94 Yes [37], [38] FUSeg dataset [28] Diabetic foot ulcer 1210 Yes [29] Sårwebben [39] Mixed -No [40] Chronic wound database [41] Chronic wound 188 Yes [42] AHZ dataset [28] Diabetic foot ulcer 1109 Yes [28] AHZ&UWM dataset [34] Mixed 538 Yes [34] publicly available [44]. Second, the primary job of medical professionals is not data collection, and the acquisition of a batch of images may be done by multiple personnel, which can lead to inconsistent standards of the collected images.…”

“…These features allow DenseNet to achieve better performance than ResNet with fewer parameters and computational costs. Bhansali et al [37] design an 8-layer CNN network for the classification of burns, which is named BurnNet. In the preprocessing stage, they use an anti-aliasing technique to adjust the image size and perform data augmentation through affine transformation.…”

Multi-Class Wound Classification via High and Low-Frequency Guidance Network

“…Currently, deep learning has been extensively applied in various fields of wound image analysis [20][21][22][23][24][25]. In a study conduct in 2017 [26], the performance of computers for skin cancer classification is compared to manual classification by dermatologists.…”