Automatic Detection of Necrotizing Fasciitis: A Dataset and Early Results

Abstract

Necrotizing Fasciitis (NF), or Necrotizing Soft-Tissue Infection (NSTI), is a rare infection that poses a significant threat to health. In the absence of a proper diagnosis, the infection can spread rapidly causing extensive tissue necrosis and death - mortality rate of 20% - 35%. Due to inadequate resources, little progress has been made for the automatic detection of NF. We have prepared a novel dataset containing images of affected human organs by NF using an internet image search. The dataset contains 693 images in total, containing raw, augmented, and non-NF images. A system has been developed for performing automated detection of NF with an Artificial Neural Network. We have evaluated the YOLOv3 object recognition model for five arrangements of our dataset and compared the performance for these different data arrangements after running each five times. The datasets were split into 80% train data and 20% test data, and for performance measures, we have taken into account the evaluation metrics: Intersection over Union (IoU) and Average Precision (AP). We obtained the highest average AP score of 57.97% for the dataset with raw data and augmentation and the highest average IoU score of 61.94% for dataset with raw data, augmentation, and negative images. The initial finding of this work can be further improved and become a substantial contribution to clinical arrangements for the diagnosis and management of NF.