Here, a machine learning tool (YOLOv5) enables the detection of Cryptosporidium microorganisms using optical and phase contrast microscope images. The two databases were processed using 520 images (optical microscopy) and 1200 images (phase contrast microscopy). It used Python libraries to label, standardize the size, and crop the images to generate the input tensors to the YOLOv5 network (s, m, and l). It implemented two experiments using randomly initialized weights in optical and phase contrast microscope images. The other two experiments used the parameters for the best training time obtained before and after retraining the models. Metrics used to assess model accuracy were mean average accuracy (mAP), confusion matrix, and the F1 scores. All three metrics confirmed that the optimal model used the best epoch of optical imaging training and retraining with phase contrast imaging. Experiments with randomly initialized weights with optical imaging showed the lowest precision for Cryptosporidium detection. The most stable model was YOLOv5m, with the best results in all categories. However, the differences between all models are lower than 2%, and YOLOv5s is the best option for Cryptosporidium detection considering the differences in computational costs of the models.

中文翻译：

---

使用 YOLOv5x 自动检测光学显微镜图像中的隐孢子虫：一项比较研究。

在这里，机器学习工具 (YOLOv5) 能够使用光学和相差显微镜图像检测隐孢子虫微生物。这两个数据库使用 520 个图像（光学显微镜）和 1200 个图像（相差显微镜）进行处理。它使用 Python 库来标记、标准化大小并裁剪图像，以生成 YOLOv5 网络的输入张量（s、m 和 l）。它使用光学和相差显微镜图像中随机初始化的权重进行了两项实验。另外两个实验使用了重新训练模型之前和之后获得的最佳训练时间的参数。用于评估模型准确性的指标包括平均准确度 (mAP)、混淆矩阵和 F1 分数。所有三个指标都证实，最佳模型使用了光学成像训练和相衬成像再训练的最佳时期。使用光学成像随机初始化权重的实验表明，隐孢子虫检测的精度最低。最稳定的模型是 YOLOv5m，在所有类别中都取得了最好的结果。然而，所有模型之间的差异均低于 2%，考虑到模型计算成本的差异，YOLOv5s 是隐孢子虫检测的最佳选择。