Kumar, S.; Arif, T.; Ahamad, G.; Chaudhary, A.A.; Khan, S.; Ali, M.A.M. An Efficient and Effective Framework for Intestinal Parasite Egg Detection Using YOLOv5. Diagnostics2023, 13, 2978.
Kumar, S.; Arif, T.; Ahamad, G.; Chaudhary, A.A.; Khan, S.; Ali, M.A.M. An Efficient and Effective Framework for Intestinal Parasite Egg Detection Using YOLOv5. Diagnostics 2023, 13, 2978.
Kumar, S.; Arif, T.; Ahamad, G.; Chaudhary, A.A.; Khan, S.; Ali, M.A.M. An Efficient and Effective Framework for Intestinal Parasite Egg Detection Using YOLOv5. Diagnostics2023, 13, 2978.
Kumar, S.; Arif, T.; Ahamad, G.; Chaudhary, A.A.; Khan, S.; Ali, M.A.M. An Efficient and Effective Framework for Intestinal Parasite Egg Detection Using YOLOv5. Diagnostics 2023, 13, 2978.
Abstract
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.
Keywords
Intestinal Parasites; Transfer learning; CNN; YOLOv5
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
