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Abstract
Lymphocytes, a type of leukocytes, play a vital role in the immune system. The precise quantification, spatial arrangement and phenotypic characterization of lymphocytes within haematological or histopathological images can serve as a diagnostic indicator of a particular lesion. Artificial neural networks, employed for the detection of lymphocytes, not only can provide support to the work of histopathologists but also enable better disease monitoring and faster analysis of the general immune system condition. In this study, the impact of visual quality on the performance of state-of-the-art algorithms for detecting lymphocytes in medical images was examined. Two datasets were used, and image modifications such as blur, sharpness, brightness, and contrast were applied to assess the performance of YOLOv5 and RetinaNet models. The study revealed that the visual quality of images exerts a substantial impact on the effectiveness of the deep learning methods in detecting lymphocytes accurately. These findings have significant implications for deep learning approaches used in digital pathology.
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Details
- Category:
- Monographic publication
- Type:
- rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
- Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Polejowska A., Sobotka M., Kalinowski M., Kordowski M., Neumann T.: Impact of Visual Image Quality on Lymphocyte Detection Using YOLOv5 and RetinaNet Algorithms// The Latest Developments and Challenges in Biomedical Engineering/ : , 2024, s.41-53
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-031-38430-1_4
- Sources of funding:
-
- Project -
- Verified by:
- Gdańsk University of Technology
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