Improving faster R-CNN generalization for intestinal parasite detection using cycle-GAN based data augmentation

Intestinal parasites pose a widespread challenge in underdeveloped and developing countries, afflicting millions of individuals. Traditional, manual light microscopes have been golden method for detecting these parasites, but they are not only expensive but also time-consuming and require specialized expertise. Recent advances in deep learning, however, have shown promise for overcoming these obstacles. The condition is that deep learning models require labeled medical imaging data, which is both scarce and costly to generate. This makes it difficult to establish universal deep learning models that required extensive amounts of data. To improve the performance of deep learning, we employed a generative adversarial network to fabricate a synthetic dataset. Our framework exploits the potential of Generative Adversarial Networks (CycleGANs) and Faster RCNN to generate new datasets and detect intestinal parasites, respectively, on images of varying quality, leading to improved model generalizability and diversity. In this experiment, we evaluated the effectiveness of Cycle Generative Adversarial Network (CycleGAN) + Faster RCNN. We employed widely-used evaluation metrics such as precision, recall, and F1-score. We demonstrated that the proposed framework effectively augmented the image dataset and improved the detection performance, with an F1-Score of 0.95 and mIoU of 0.97 are achieved, which is better than without data augmentation. We show that this state-of-the-art approach sets the stage for further advancements in the field of medical image analysis. Additionally, we have built a new dataset, which is now publicly accessible, offering a broader range of classes and variability for future research and development. In this paper, we proposed an improved Faster-RCNN, aimed at detecting intestinal parasites in complicated situations. We focus on dataset enhancement, with the aim of achieving both high performance and system generalization. The model was built utilizing publicly available datasets. Our proposed strategy has outperformed existing state-of-the-art.