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.

Pneumonia is the biggest cause of death in children below the age 5 years, as well as the main source of death worldwide but mostly in developing countries. The detection of pneumonia is usually done by highly-trained doctors looking at a chest X-Ray radiograph. This is a time-consuming process that frequently results in radiologists disagreeing. Clinical-aided techniques showed promise in terms of increasing diagnostic precision. There was no single clinical characteristic that was sufficient to definitively diagnose pneumonia. The novel point of test care for the treatment of bacterial pneumonia can help to reach a sustainable amount of correctness, but adding clinical characters in a decision tree would improve treatment result. We use clinical symptoms like compress and vanishing deep twist machine learning, supplementations, and also we use aggregate learning to construct a computational strategy for detecting pneumonia in children. Early diagnosis can decrease the death rate in children. The WHO criteria for diagnosing clinical pneumonia are applied for the precision.