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

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

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.

使用深度学习对Detection传皮肤病变进行自动检测和分类

在全球范围内，壁虱是传播各种细菌，病毒和寄生虫疾病的元凶。在过去的十年中，within传播疾病的发病率急剧上升，仅在美国，每年的莱姆病病例就猛增至30万。.. 因此，为改进tick传播疾病的病灶识别方法和诊断做出更多努力至关重要。这项研究的目的是建立在Burlina等人使用的方法的基础上。通过使用各种卷积神经网络模型来检测tick传播的皮肤病变。我们通过从Google获取七种不同语言的图像来扩展数据输入，以测试这是否可以使训练数据多样化并提高皮肤病变检测的准确性。最终的数据集包含近6,080张图像，并接受了多种体系结构的培训（ResNet 34，ResNet 50，VGG 19和Dense Net 121）。通过在DenseNet 121架构上训练的模型，我们获得了80.72％的准确性。 （阅读更多）