Benchmarking Deep Learning Frameworks for Automated Diagnosis of Ocular
  Toxoplasmosis: A Comprehensive Approach to Classification and Segmentation
Syed Samiul Alam Samiul Based Shuvo Shams Nafisa Ali Fardeen Ahmed Arbil Chakma Yeong Min Jang
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.