Intestinal parasitic infections pose a grave threat to human health, particularly in tropical and subtropical regions. The traditional manual microscopy system of intestinal parasite detection remains the gold standard procedure for diagnosing parasite cysts or eggs. This approach is costly, time-consuming (30 min per sample), highly tedious, and requires a specialist. However, computer vision, based on deep learning, has made great strides in recent years. Despite the significant advances in deep convolutional neural network-based architectures, little research has been conducted to explore these techniques’ potential in parasitology, specifically for intestinal parasites. This research presents a novel proposal for state-of-the-art transfer learning architecture for the detection and classification of intestinal parasite eggs from images. The ultimate goal is to ensure prompt treatment for patients while also alleviating the burden on experts. Our approach comprised two main stages: image pre-processing and augmentation in the first stage, and YOLOv5 algorithms for detection and classification in the second stage, followed by performance comparison based on different parameters. Remarkably, our algorithms achieved a mean average precision of approximately 97% and a detection time of only 8.5 ms per sample for a dataset of 5393 intestinal parasite images. This innovative approach holds tremendous potential to form a solid theoretical basis for real-time detection and classification in routine clinical examinations, addressing the increasing demand and accelerating the diagnostic process. Our research contributes to the development of cutting-edge technologies for the efficient and accurate detection of intestinal parasite eggs, advancing the field of medical imaging and diagnosis.

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.

Medical images are important part of modern healthcare, helping in disease diagnosis, treatment planning, and monitoring of patients. In healthcare applications, biomedical images comprise approximately 90% data. Recent advancements in deep learning have contributed novel techniques in image analysis, and one of these techniques, Transformer, which was mostly used for natural language processing, has emerged as one of the useful tools in the field of medical image analysis. In this chapter, we investigate several applications of Transformers in the field of biomedical imaging, emphasizing on their capacity to improve image classification, detection, segmentation, and synthesis. This chapter covers the fundamental concepts behind Transformers and their architecture, along with self-attention techniques. It emphasizes on adapting pre-trained models toward medical imaging applications, minimizing the requirement for huge amounts of labeled datasets and also enhancing model generalization. The primary applications of Transformers in medical images, such as disease detection, an atomically segmentation, and image synthesis, are discussed. In conclusion, the fascinating integration of Transformers and medical imaging has the potential to revolutionize the healthcare domain along with improving diagnostic accuracy, reducing human intervention, and enhancing patient care. This chapter contributes to a comprehensive understanding of how Transformers act as the state-of-the-art in biomedical imaging and inspire further exploration at this exciting enhancement of deep learning and healthcare. <!----> <!----> <!----> <!---->

Intestinal parasitic infections pose a grave threat to human health, particularly in tropical and subtropical regions. The traditional manual microscopy system of intestinal parasites detection remains the golden standard procedure for the diagnosis of parasites cyst or eggs, but this approach is costly time-consuming (30min/sample), highly tedious, and also required specialist. However, computer vision based on deep learning has made great stride in recent time. Despite the significant advances in deep convolutional neural network-based architectures, little research has been conducted to explore the potential of these techniques in the field of parasitology, specifically for intestinal parasites. Therefore, the goal of this research is to evaluate the performance of proposed state-of-the-art transfer learning architecture for detecting and classifying intestinal parasite eggs from images. We would ensure that patients receive prompt treatment while also relieving experts of extra work if we used such an architecture. Here, in stage first, we applied image pre-processing and augmentation to the dataset, and in stage second, we utilized the YOLOv5 algorithms for detection and classification and then compared their performance based on different parameters. Our algorithms achieved a mean average precision of 97% approximatiely and 8.5 ms detection time per sample for 5,393 intestinal parasite images. Thus, this approach may form a solid theoretical basis for real-time detection and classification in routine clinical examinations while accelerating the process to satisfy increasing demand.