AI-Driven Automated Blood Cell Anomaly Detection: Enhancing Diagnostics and Telehealth in Hematology
Hematology plays a critical role in diagnosing and managing a wide range of blood-related disorders. The manual interpretation of blood smear images, however, is time-consuming and highly dependent on expert availability. Moreover, it is particularly challenging in remote and resource-limited settin...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/11/5/157 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849327349510701056 |
|---|---|
| author | Sami Naouali Oussama El Othmani |
| author_facet | Sami Naouali Oussama El Othmani |
| author_sort | Sami Naouali |
| collection | DOAJ |
| description | Hematology plays a critical role in diagnosing and managing a wide range of blood-related disorders. The manual interpretation of blood smear images, however, is time-consuming and highly dependent on expert availability. Moreover, it is particularly challenging in remote and resource-limited settings. In this study, we present an AI-driven system for automated blood cell anomaly detection, combining computer vision and machine learning models to support efficient diagnostics in hematology and telehealth contexts. Our architecture integrates segmentation (YOLOv11), classification (ResNet50), transfer learning, and zero-shot learning to identify and categorize cell types and abnormalities from blood smear images. Evaluated on real annotated samples, the system achieved high performance, with a precision of 0.98, recall of 0.99, and F1 score of 0.98. These results highlight the potential of the proposed system to enhance remote diagnostic capabilities and support clinical decision making in underserved regions. |
| format | Article |
| id | doaj-art-959f624deeee4f16b4cdb94205f3476c |
| institution | Kabale University |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-959f624deeee4f16b4cdb94205f3476c2025-08-20T03:47:54ZengMDPI AGJournal of Imaging2313-433X2025-05-0111515710.3390/jimaging11050157AI-Driven Automated Blood Cell Anomaly Detection: Enhancing Diagnostics and Telehealth in HematologySami Naouali0Oussama El Othmani1Information Systems Department, College of Computer Science and Information Technology, King Faisal University, Al Ahsa 31982, Saudi ArabiaInformation Systems Department, Military Academy of Fondouk Jedid, Nabeul 8012, TunisiaHematology plays a critical role in diagnosing and managing a wide range of blood-related disorders. The manual interpretation of blood smear images, however, is time-consuming and highly dependent on expert availability. Moreover, it is particularly challenging in remote and resource-limited settings. In this study, we present an AI-driven system for automated blood cell anomaly detection, combining computer vision and machine learning models to support efficient diagnostics in hematology and telehealth contexts. Our architecture integrates segmentation (YOLOv11), classification (ResNet50), transfer learning, and zero-shot learning to identify and categorize cell types and abnormalities from blood smear images. Evaluated on real annotated samples, the system achieved high performance, with a precision of 0.98, recall of 0.99, and F1 score of 0.98. These results highlight the potential of the proposed system to enhance remote diagnostic capabilities and support clinical decision making in underserved regions.https://www.mdpi.com/2313-433X/11/5/157artificial intelligenceblood cell analysismachine learninghematologymedical imagingtelehealth |
| spellingShingle | Sami Naouali Oussama El Othmani AI-Driven Automated Blood Cell Anomaly Detection: Enhancing Diagnostics and Telehealth in Hematology Journal of Imaging artificial intelligence blood cell analysis machine learning hematology medical imaging telehealth |
| title | AI-Driven Automated Blood Cell Anomaly Detection: Enhancing Diagnostics and Telehealth in Hematology |
| title_full | AI-Driven Automated Blood Cell Anomaly Detection: Enhancing Diagnostics and Telehealth in Hematology |
| title_fullStr | AI-Driven Automated Blood Cell Anomaly Detection: Enhancing Diagnostics and Telehealth in Hematology |
| title_full_unstemmed | AI-Driven Automated Blood Cell Anomaly Detection: Enhancing Diagnostics and Telehealth in Hematology |
| title_short | AI-Driven Automated Blood Cell Anomaly Detection: Enhancing Diagnostics and Telehealth in Hematology |
| title_sort | ai driven automated blood cell anomaly detection enhancing diagnostics and telehealth in hematology |
| topic | artificial intelligence blood cell analysis machine learning hematology medical imaging telehealth |
| url | https://www.mdpi.com/2313-433X/11/5/157 |
| work_keys_str_mv | AT saminaouali aidrivenautomatedbloodcellanomalydetectionenhancingdiagnosticsandtelehealthinhematology AT oussamaelothmani aidrivenautomatedbloodcellanomalydetectionenhancingdiagnosticsandtelehealthinhematology |