Image-Based Detection and Classification of Malaria Parasites and Leukocytes with Quality Assessment of Romanowsky-Stained Blood Smears
Malaria remains a global health concern, with 249 million cases and 608,000 deaths being reported by the WHO in 2022. Traditional diagnostic methods often struggle with inconsistent stain quality, lighting variations, and limited resources in endemic regions, making manual detection time-intensive a...
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2025-01-01
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| author | Jhonathan Sora-Cardenas Wendy M. Fong-Amaris Cesar A. Salazar-Centeno Alejandro Castañeda Oscar D. Martínez-Bernal Daniel R. Suárez Carol Martínez |
| author_facet | Jhonathan Sora-Cardenas Wendy M. Fong-Amaris Cesar A. Salazar-Centeno Alejandro Castañeda Oscar D. Martínez-Bernal Daniel R. Suárez Carol Martínez |
| author_sort | Jhonathan Sora-Cardenas |
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| description | Malaria remains a global health concern, with 249 million cases and 608,000 deaths being reported by the WHO in 2022. Traditional diagnostic methods often struggle with inconsistent stain quality, lighting variations, and limited resources in endemic regions, making manual detection time-intensive and error-prone. This study introduces an automated system for analyzing Romanowsky-stained thick blood smears, focusing on image quality evaluation, leukocyte detection, and malaria parasite classification. Using a dataset of 1000 clinically diagnosed images, we applied feature extraction techniques, including histogram bins and texture analysis with the gray level co-occurrence matrix (GLCM), alongside support vector machines (SVMs), for image quality assessment. Leukocyte detection employed optimal thresholding segmentation utility (OTSU) thresholding, binary masking, and erosion, followed by the connected components algorithm. Parasite detection used high-intensity region selection and adaptive bounding boxes, followed by a custom convolutional neural network (CNN) for candidate identification. A second CNN classified parasites into trophozoites, schizonts, and gametocytes. The system achieved an F1-score of 95% for image quality evaluation, 88.92% for leukocyte detection, and 82.10% for parasite detection. The F1-score—a metric balancing precision (correctly identified positives) and recall (correctly detected instances out of actual positives)—is especially valuable for assessing models on imbalanced datasets. In parasite stage classification, CNN achieved F1-scores of 85% for trophozoites, 88% for schizonts, and 83% for gametocytes. This study introduces a robust and scalable automated system that addresses critical challenges in malaria diagnosis by integrating advanced image quality assessment and deep learning techniques for parasite detection and classification. This system’s adaptability to low-resource settings underscores its potential to improve malaria diagnostics globally. |
| format | Article |
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| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-12c9c83c18bc4865bc21631912b94ac62025-01-24T13:48:45ZengMDPI AGSensors1424-82202025-01-0125239010.3390/s25020390Image-Based Detection and Classification of Malaria Parasites and Leukocytes with Quality Assessment of Romanowsky-Stained Blood SmearsJhonathan Sora-Cardenas0Wendy M. Fong-Amaris1Cesar A. Salazar-Centeno2Alejandro Castañeda3Oscar D. Martínez-Bernal4Daniel R. Suárez5Carol Martínez6Faculty of Engineering, Pontificia Universidad Javeriana, Bogotá 110311, ColombiaFaculty of Engineering, Pontificia Universidad Javeriana, Bogotá 110311, ColombiaFaculty of Engineering, Pontificia Universidad Javeriana, Bogotá 110311, ColombiaFaculty of Engineering, Pontificia Universidad Javeriana, Bogotá 110311, ColombiaFaculty of Engineering, Pontificia Universidad Javeriana, Bogotá 110311, ColombiaFaculty of Engineering, Pontificia Universidad Javeriana, Bogotá 110311, ColombiaSpace Robotics Research Group (SpaceR), Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, L-1855 Luxembourg, LuxembourgMalaria remains a global health concern, with 249 million cases and 608,000 deaths being reported by the WHO in 2022. Traditional diagnostic methods often struggle with inconsistent stain quality, lighting variations, and limited resources in endemic regions, making manual detection time-intensive and error-prone. This study introduces an automated system for analyzing Romanowsky-stained thick blood smears, focusing on image quality evaluation, leukocyte detection, and malaria parasite classification. Using a dataset of 1000 clinically diagnosed images, we applied feature extraction techniques, including histogram bins and texture analysis with the gray level co-occurrence matrix (GLCM), alongside support vector machines (SVMs), for image quality assessment. Leukocyte detection employed optimal thresholding segmentation utility (OTSU) thresholding, binary masking, and erosion, followed by the connected components algorithm. Parasite detection used high-intensity region selection and adaptive bounding boxes, followed by a custom convolutional neural network (CNN) for candidate identification. A second CNN classified parasites into trophozoites, schizonts, and gametocytes. The system achieved an F1-score of 95% for image quality evaluation, 88.92% for leukocyte detection, and 82.10% for parasite detection. The F1-score—a metric balancing precision (correctly identified positives) and recall (correctly detected instances out of actual positives)—is especially valuable for assessing models on imbalanced datasets. In parasite stage classification, CNN achieved F1-scores of 85% for trophozoites, 88% for schizonts, and 83% for gametocytes. This study introduces a robust and scalable automated system that addresses critical challenges in malaria diagnosis by integrating advanced image quality assessment and deep learning techniques for parasite detection and classification. This system’s adaptability to low-resource settings underscores its potential to improve malaria diagnostics globally.https://www.mdpi.com/1424-8220/25/2/390malaria diagnosisthick blood smearsimage processingsupport vector machinesconvolutional neural networksdeep learning |
| spellingShingle | Jhonathan Sora-Cardenas Wendy M. Fong-Amaris Cesar A. Salazar-Centeno Alejandro Castañeda Oscar D. Martínez-Bernal Daniel R. Suárez Carol Martínez Image-Based Detection and Classification of Malaria Parasites and Leukocytes with Quality Assessment of Romanowsky-Stained Blood Smears Sensors malaria diagnosis thick blood smears image processing support vector machines convolutional neural networks deep learning |
| title | Image-Based Detection and Classification of Malaria Parasites and Leukocytes with Quality Assessment of Romanowsky-Stained Blood Smears |
| title_full | Image-Based Detection and Classification of Malaria Parasites and Leukocytes with Quality Assessment of Romanowsky-Stained Blood Smears |
| title_fullStr | Image-Based Detection and Classification of Malaria Parasites and Leukocytes with Quality Assessment of Romanowsky-Stained Blood Smears |
| title_full_unstemmed | Image-Based Detection and Classification of Malaria Parasites and Leukocytes with Quality Assessment of Romanowsky-Stained Blood Smears |
| title_short | Image-Based Detection and Classification of Malaria Parasites and Leukocytes with Quality Assessment of Romanowsky-Stained Blood Smears |
| title_sort | image based detection and classification of malaria parasites and leukocytes with quality assessment of romanowsky stained blood smears |
| topic | malaria diagnosis thick blood smears image processing support vector machines convolutional neural networks deep learning |
| url | https://www.mdpi.com/1424-8220/25/2/390 |
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