Smartphone cameras and digital devices are increasingly used in the capture of tick images by the public as citizen scientists, and rapid advances in deep learning and computer vision has enabled brand new image recognition models to be trained. However, there is currently no web-based or mobile application that supports automated classification of tick images. The purpose of this study was to compare the accuracy of a deep learning model pre-trained with millions of annotated images in Imagenet, against a shallow custom-build convolutional neural network (CNN) model for the classification of common hard ticks present in anthropic areas from northeastern USA. We created a dataset of approximately 2000 images of four tick species (Ixodes scapularis, Dermacentor variabilis, Amblyomma americanum and Haemaphysalis sp.), two sexes (male, female) and two life stages (adult, nymph). We used these tick images to train two separate CNN models – ResNet-50 and a simple shallow custom-built. We evaluated our models’ performance on an independent subset of tick images not seen during training. Compared to the ResNet-50 model, the small shallow custom-built model had higher training (99.7%) and validation (99.1%) accuracies. When tested with new tick image data, the shallow custom-built model yielded higher mean prediction accuracy (80%), greater confidence of true detection (88.7%) and lower mean response time (3.64 s). These results demonstrate that, with limited data size for model training, a simple shallow custom-built CNN model has great prospects for use in the classification of common hard ticks present in anthropic areas from northeastern USA.

作为公民科学家的公众越来越多地使用智能手机摄像头和数字设备来捕捉蜱虫图像，而深度学习和计算机视觉的快速发展使全新的图像识别模型得以训练。但是，目前没有支持蜱图像自动分类的基于 Web 或移动的应用程序。本研究的目的是比较在 Imagenet 中使用数百万个带注释的图像预训练的深度学习模型与浅层定制卷积神经网络 (CNN) 模型的准确性，该模型用于对人类区域中存在的常见硬蜱进行分类来自美国东北部。我们创建了一个包含大约 2000 张图像的数据集，其中包含四种蜱类（肩胛硬蜱、变异皮蜱、Amblyomma americanum和Haemaphysalis sp 。)、两种性别（雄性、雌性）和两个生命阶段（成虫、若虫）。我们使用这些刻度图像来训练两个独立的 CNN 模型——ResNet-50 和一个简单的浅层定制模型。我们在训练期间未看到的独立蜱图像子集上评估了我们的模型的性能。与 ResNet-50 模型相比，小型浅层定制模型具有更高的训练 (99.7%) 和验证 (99.1%) 准确率。当使用新的蜱图像数据进行测试时，浅层定制模型产生了更高的平均预测精度 (80%)、更高的真实检测置信度 (88.7%) 和更低的平均响应时间 (3.64 秒)。这些结果表明，在模型训练数据量有限的情况下，一个简单的浅层定制 CNN 模型在美国东北部人类活动地区常见的硬蜱分类中具有广阔的前景。