  Lyme disease which is one of the most common infectious vector-borne diseases
manifests itself in most cases with erythema migrans (EM) skin lesions. Recent
studies show that convolutional neural networks (CNNs) perform well to identify
skin lesions from images. Lightweight CNN based pre-scanner applications for
resource-constrained mobile devices can help users with early diagnosis of Lyme
disease and prevent the transition to a severe late form thanks to appropriate
antibiotic therapy. Also, resource-intensive CNN based robust computer
applications can assist non-expert practitioners with an accurate diagnosis.
The main objective of this study is to extensively analyze the effectiveness of
CNNs for diagnosing Lyme disease from images and to find out the best CNN
architectures considering resource constraints. First, we created an EM dataset
with the help of expert dermatologists from Clermont-Ferrand University
Hospital Center of France. Second, we benchmarked this dataset for twenty-three
CNN architectures customized from VGG, ResNet, DenseNet, MobileNet, Xception,
NASNet, and EfficientNet architectures in terms of predictive performance,
computational complexity, and statistical significance. Third, to improve the
performance of the CNNs, we used custom transfer learning from ImageNet
pre-trained models as well as pre-trained the CNNs with the skin lesion dataset
HAM10000. Fourth, for model explainability, we utilized Gradient-weighted Class
Activation Mapping to visualize the regions of input that are significant to
the CNNs for making predictions. Fifth, we provided guidelines for model
selection based on predictive performance and computational complexity.
