Automated detection of pinworm parasite eggs using YOLO convolutional block attention module for enhanced microscopic...

Parasitic infections remain a major public health concern, particularly in healthcare and community settings where rapid and accurate diagnosis is essential for effective treatment and prevention. Traditional parasite detection methods rely on manual microscopic examinations, which are time-consuming, labor-intensive, and susceptible to human error. Recent advancements in automated microscopic imaging and deep learning offer promising solutions to enhance diagnostic accuracy and efficiency.

This study proposes a novel framework, the YOLO Convolutional Block Attention Module (YCBAM), to automate the detection of pinworm parasite eggs in microscopic images. The YCBAM architecture integrates YOLO with self-attention mechanisms and the Convolutional Block Attention Module (CBAM), enabling precise identification and localization of parasitic elements in challenging imaging conditions.

Experimental evaluation of the YCBAM model demonstrated a precision of 0.9971, a recall of 0.9934, and a training box loss of 1.1410, indicating efficient learning and convergence. The model achieved a mean Average Precision (mAP) of 0.9950 at an IoU threshold of 0.50 and a mAP50-95 score of 0.6531 across varying IoU thresholds, confirming its superior detection performance. The integration of YOLO with self-attention and CBAM significantly improves the automated detection of pinworm eggs, offering a highly accurate and reliable diagnostic tool for medical parasitology. This framework has the potential to reduce diagnostic errors, save time, and support healthcare professionals in making informed decisions.