An Efficient and Effective Framework for Intestinal Parasite Egg Detection Using YOLOv5
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-co...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/18/2978 |
| _version_ | 1797580594210668544 |
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| author | Satish Kumar Tasleem Arif Gulfam Ahamad Anis Ahmad Chaudhary Salahuddin Khan Mohamed A. M. Ali |
| author_facet | Satish Kumar Tasleem Arif Gulfam Ahamad Anis Ahmad Chaudhary Salahuddin Khan Mohamed A. M. Ali |
| author_sort | Satish Kumar |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T22:51:57Z |
| format | Article |
| id | doaj.art-35db90d3ca4745329445a72f1968e73d |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T22:51:57Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-35db90d3ca4745329445a72f1968e73d2023-11-19T10:14:14ZengMDPI AGDiagnostics2075-44182023-09-011318297810.3390/diagnostics13182978An Efficient and Effective Framework for Intestinal Parasite Egg Detection Using YOLOv5Satish Kumar0Tasleem Arif1Gulfam Ahamad2Anis Ahmad Chaudhary3Salahuddin Khan4Mohamed A. M. Ali5Department of Information Technology, BGSB University, Rajouri 185131, IndiaDepartment of Information Technology, BGSB University, Rajouri 185131, IndiaDepartment of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri 185131, IndiaDepartment of Biology, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11623, Saudi ArabiaDepartment of Biochemistry, College of Medicine, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11623, Saudi ArabiaDepartment of Biology, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11623, Saudi ArabiaIntestinal 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.https://www.mdpi.com/2075-4418/13/18/2978intestinal parasitestransfer learningCNNYOLOv5 |
| spellingShingle | Satish Kumar Tasleem Arif Gulfam Ahamad Anis Ahmad Chaudhary Salahuddin Khan Mohamed A. M. Ali An Efficient and Effective Framework for Intestinal Parasite Egg Detection Using YOLOv5 Diagnostics intestinal parasites transfer learning CNN YOLOv5 |
| title | An Efficient and Effective Framework for Intestinal Parasite Egg Detection Using YOLOv5 |
| title_full | An Efficient and Effective Framework for Intestinal Parasite Egg Detection Using YOLOv5 |
| title_fullStr | An Efficient and Effective Framework for Intestinal Parasite Egg Detection Using YOLOv5 |
| title_full_unstemmed | An Efficient and Effective Framework for Intestinal Parasite Egg Detection Using YOLOv5 |
| title_short | An Efficient and Effective Framework for Intestinal Parasite Egg Detection Using YOLOv5 |
| title_sort | efficient and effective framework for intestinal parasite egg detection using yolov5 |
| topic | intestinal parasites transfer learning CNN YOLOv5 |
| url | https://www.mdpi.com/2075-4418/13/18/2978 |
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