Efficient diagnostic model for iron deficiency anaemia detection: a comparison of CNN and object detection algorithms in peripheral blood smear images
Iron Deficiency Anaemia (IDA) is the most prevalent form of anaemia, affecting 24.8% of the global population. An examination of the complete blood count (CBC) is performed to determine general health and the presence of illnesses. Accurate and timely diagnosis of IDA is essential for proper treatme...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-01-01
|
| Series: | Automatika |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2024.2433868 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832582137048465408 |
|---|---|
| author | Navya K. T Seemitr Verma Keerthana Prasad Brij Mohan Kumar Singh |
| author_facet | Navya K. T Seemitr Verma Keerthana Prasad Brij Mohan Kumar Singh |
| author_sort | Navya K. T |
| collection | DOAJ |
| description | Iron Deficiency Anaemia (IDA) is the most prevalent form of anaemia, affecting 24.8% of the global population. An examination of the complete blood count (CBC) is performed to determine general health and the presence of illnesses. Accurate and timely diagnosis of IDA is essential for proper treatment, yet traditional methods can be time-consuming and costly. This study uses machine learning and computer vision techniques for the automatic identification of hypochromic microcytes from Peripheral Blood Smear (PBS) images to improve IDA diagnosis. Two approaches were implemented: first, a ResNet50 model was used to classify PBS images as Normal or IDA; second, the YOLOv7 object detection model was employed to localize hypochromic microcytes within the images. The YOLOv7 model was tested on 17 images containing 425 instances of hypochromic microcytes and demonstrated superior performance, achieving a test mean Average Precision (mAP) of 89% with faster inference times than ResNet50. By providing localized detection of hypochromic microcytes, YOLOv7 enhances diagnostic accuracy and speed compared to image-level classification. This study highlights the potential of object detection models for improving automated anaemia diagnosis, with implications for faster and more cost-effective healthcare solutions. |
| format | Article |
| id | doaj-art-f8686bddb95643178968806720029059 |
| institution | Kabale University |
| issn | 0005-1144 1848-3380 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj-art-f8686bddb956431789688067200290592025-01-30T05:18:09ZengTaylor & Francis GroupAutomatika0005-11441848-33802025-01-0166111510.1080/00051144.2024.2433868Efficient diagnostic model for iron deficiency anaemia detection: a comparison of CNN and object detection algorithms in peripheral blood smear imagesNavya K. T0Seemitr Verma1Keerthana Prasad2Brij Mohan Kumar Singh3Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Hematology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, IndiaManipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Pathology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, IndiaIron Deficiency Anaemia (IDA) is the most prevalent form of anaemia, affecting 24.8% of the global population. An examination of the complete blood count (CBC) is performed to determine general health and the presence of illnesses. Accurate and timely diagnosis of IDA is essential for proper treatment, yet traditional methods can be time-consuming and costly. This study uses machine learning and computer vision techniques for the automatic identification of hypochromic microcytes from Peripheral Blood Smear (PBS) images to improve IDA diagnosis. Two approaches were implemented: first, a ResNet50 model was used to classify PBS images as Normal or IDA; second, the YOLOv7 object detection model was employed to localize hypochromic microcytes within the images. The YOLOv7 model was tested on 17 images containing 425 instances of hypochromic microcytes and demonstrated superior performance, achieving a test mean Average Precision (mAP) of 89% with faster inference times than ResNet50. By providing localized detection of hypochromic microcytes, YOLOv7 enhances diagnostic accuracy and speed compared to image-level classification. This study highlights the potential of object detection models for improving automated anaemia diagnosis, with implications for faster and more cost-effective healthcare solutions.https://www.tandfonline.com/doi/10.1080/00051144.2024.2433868Peripheral blood smearred blood cellsiron deficiency anaemiahypochromic microcytesobject detection |
| spellingShingle | Navya K. T Seemitr Verma Keerthana Prasad Brij Mohan Kumar Singh Efficient diagnostic model for iron deficiency anaemia detection: a comparison of CNN and object detection algorithms in peripheral blood smear images Automatika Peripheral blood smear red blood cells iron deficiency anaemia hypochromic microcytes object detection |
| title | Efficient diagnostic model for iron deficiency anaemia detection: a comparison of CNN and object detection algorithms in peripheral blood smear images |
| title_full | Efficient diagnostic model for iron deficiency anaemia detection: a comparison of CNN and object detection algorithms in peripheral blood smear images |
| title_fullStr | Efficient diagnostic model for iron deficiency anaemia detection: a comparison of CNN and object detection algorithms in peripheral blood smear images |
| title_full_unstemmed | Efficient diagnostic model for iron deficiency anaemia detection: a comparison of CNN and object detection algorithms in peripheral blood smear images |
| title_short | Efficient diagnostic model for iron deficiency anaemia detection: a comparison of CNN and object detection algorithms in peripheral blood smear images |
| title_sort | efficient diagnostic model for iron deficiency anaemia detection a comparison of cnn and object detection algorithms in peripheral blood smear images |
| topic | Peripheral blood smear red blood cells iron deficiency anaemia hypochromic microcytes object detection |
| url | https://www.tandfonline.com/doi/10.1080/00051144.2024.2433868 |
| work_keys_str_mv | AT navyakt efficientdiagnosticmodelforirondeficiencyanaemiadetectionacomparisonofcnnandobjectdetectionalgorithmsinperipheralbloodsmearimages AT seemitrverma efficientdiagnosticmodelforirondeficiencyanaemiadetectionacomparisonofcnnandobjectdetectionalgorithmsinperipheralbloodsmearimages AT keerthanaprasad efficientdiagnosticmodelforirondeficiencyanaemiadetectionacomparisonofcnnandobjectdetectionalgorithmsinperipheralbloodsmearimages AT brijmohankumarsingh efficientdiagnosticmodelforirondeficiencyanaemiadetectionacomparisonofcnnandobjectdetectionalgorithmsinperipheralbloodsmearimages |