Automatic Detection and Classification of Tick-borne Skin Lesions using Deep Learning
This work provides an incremental improvement in automated diagnostics for tick-borne illnesses, which could benefit medical professionals and patients in early detection.
This study addressed the challenge of identifying tick-borne skin lesions by training various convolutional neural network models on a dataset of nearly 6,080 images. The best performing model, based on the DenseNet 121 architecture, achieved an accuracy of 80.72% in detecting these lesions.
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.