Ocular Toxoplasmosis (OT), is a common eye infection caused by T. gondii that can cause vision problems. Diagnosis is typically done through a clinical examination and imaging, but these methods can be complicated and costly, requiring trained personnel. To address this issue, we have created a benchmark study that evaluates the effectiveness of existing pre-trained networks using transfer learning techniques to detect OT from fundus images. Furthermore, we have also analysed the performance of transfer-learning based segmentation networks to segment lesions in the images. This research seeks to provide a guide for future researchers looking to utilise DL techniques and develop a cheap, automated, easy-to-use, and accurate diagnostic method. We have performed in-depth analysis of different feature extraction techniques in order to find the most optimal one for OT classification and segmentation of lesions. For classification tasks, we have evaluated pre-trained models such as VGG16, MobileNetV2, InceptionV3, ResNet50, and DenseNet121 models. Among them, MobileNetV2 outperformed all other models in terms of Accuracy (Acc), Recall, and F1 Score outperforming the second-best model, InceptionV3 by 0.7% higher Acc. However, DenseNet121 achieved the best result in terms of Precision, which was 0.1% higher than MobileNetv2. For the segmentation task, this work has exploited U-Net architecture. In order to utilize transfer learning the encoder block of the traditional U-Net was replaced by MobileNetV2, InceptionV3, ResNet34, and VGG16 to evaluate different architectures moreover two different two different loss functions (Dice loss and Jaccard loss) were exploited in order to find the most optimal one. The MobileNetV2/U-Net outperformed ResNet34 by 0.5% and 2.1% in terms of Acc and Dice Score, respectively when Jaccard loss function is employed during the training.

翻译：眼弓形虫病（OT）是一种由刚地弓形虫引起的常见眼部感染，可能导致视力问题。其诊断通常通过临床检查和影像学手段完成，但这些方法复杂且成本高昂，需要专业人员的参与。为解决这一问题，我们开展了一项基准研究，评估基于迁移学习技术的现有预训练网络从眼底图像中检测OT的有效性。此外，我们还分析了基于迁移学习的分割网络对图像中病灶进行分割的性能。本研究旨在为未来研究人员利用深度学习技术开发低成本、自动化、易操作且高精度的诊断方法提供指导。我们深入分析了不同特征提取技术，以寻找最适合OT分类和病灶分割的方法。在分类任务中，我们评估了VGG16、MobileNetV2、InceptionV3、ResNet50和DenseNet121等预训练模型。其中，MobileNetV2在准确率（Acc）、召回率和F1分数上均优于其他模型，其准确率比第二名的InceptionV3高0.7%。然而，DenseNet121在精确率上表现最佳，比MobileNetV2高0.1%。在分割任务中，本研究采用了U-Net架构。为实现迁移学习，我们将传统U-Net的编码器模块替换为MobileNetV2、InceptionV3、ResNet34和VGG16，以评估不同架构；同时，还使用了两种不同的损失函数（Dice损失和Jaccard损失），以寻找最优方案。结果表明，当在训练中采用Jaccard损失函数时，MobileNetV2/U-Net在准确率上比ResNet34高0.5%，在Dice分数上高2.1%。