Complete Blood Cell Detection and Counting Based on Deep Neural Networks
Source abstract
Abstract
Complete blood cell (CBC) counting has played a vital role in general medical examination. Common approaches, such as traditional manual counting and automated analyzers, were heavily influenced by the operation of medical professionals. In recent years, computer-aided object detection using deep learning algorithms has been successfully applied in many different visual tasks. In this paper, we propose a deep neural network-based architecture to accurately detect and count blood cells on blood smear images. A public BCCD (Blood Cell Count and Detection) dataset is used for the performance evaluation of our architecture. It is not uncommon that blood smear images are in low resolution, and blood cells on them are blurry and overlapping. The original images were preprocessed, including image augmentation, enlargement, sharpening, and blurring. With different settings in the proposed architecture, five models are constructed herein. We compare their performance on red blood cells (RBC), white blood cells (WBC), and platelet detection and deeply investigate the factors related to their performance. The experiment results show that our models can recognize blood cells accurately when blood cells are not heavily overlapping.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Imaging for Blood Diseases · Retinal Imaging and Analysis · Anomaly Detection Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Management of Technology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
E.SUN Commercial Bank, Ltd., Taipei 105, Taiwan
Department of Information Management, Tunghai University, Taichung 407224, Taiwan
Funding
National Science and Technology Council, Taiwan
References (50)
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