Automatic Detection and Classification of Tick-borne Skin Lesions using Deep Learning

TL;DR

This study employs various convolutional neural networks, including DenseNet 121, to detect tick-borne skin lesions from a diverse dataset of nearly 6,080 images, achieving over 80% accuracy to aid in disease diagnosis.

Contribution

It introduces a multi-architecture deep learning approach with diversified data sources to improve tick-borne skin lesion detection accuracy.

Findings

DenseNet 121 achieved 80.72% accuracy

Using multilingual image data improved model robustness

Multiple CNN architectures were evaluated for detection performance

Abstract

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.