  Around the globe, ticks are the culprit of transmitting a variety of
bacterial, viral and parasitic diseases. The incidence of tick-borne diseases
has drastically increased within the last decade, with annual cases of Lyme
disease soaring to an estimated 300,000 in the United States alone. As a
result, more efforts in improving lesion identification approaches and
diagnostics for tick-borne illnesses is critical. The objective for this study
is to build upon the approach used by Burlina et al. by using a variety of
convolutional neural network models to detect tick-borne skin lesions. We
expanded the data inputs by acquiring images from Google in seven different
languages to test if this would diversify training data and improve the
accuracy of skin lesion detection. The final dataset included nearly 6,080
images and was trained on a combination of architectures (ResNet 34, ResNet 50,
VGG 19, and Dense Net 121). We obtained an accuracy of 80.72% with our model
trained on the DenseNet 121 architecture.
