Advances in blood cell detection and classification: A review of deep learning and object detection techniques

• Main Authors: Markose, Nisha, Elayidom, M. Sudheep
• Format: Conference Proceeding
• Language: English
• Subjects: Algorithms; Blood; Blood cells; Classification; Deep learning; Error detection; Image acquisition; Image processing; Image segmentation; Machine learning; Morphology; Object recognition; Performance measurement
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0227440

In the diagnosis and treatment of a patient, blood cell detection and classification is a critical activity. Skilled lab technicians manually inspect the blood cells in a conventional method, which is time-consuming and error-prone. With the recent advancements in image processing, deep learning and object detection techniques has acquired larger attention for automating the procedure of blood cell detection and classification. This article focuses on how image processing and machine learning can be used to morphologically characterise and recognise cell pictures collected from peripheral blood smears. Image processing techniques for blood cell detection are typically based on thresholding, segmentation and morphological operations. Machine learning algorithms can learn from data and adapt to new conditions allowing for more accurate and robust blood cell detection. Deep learning algorithms can learn to extract relevant features from raw image data, eliminating the need for manual feature engineering. Deep learning methodology is superior to traditional image processing methods in literature. This paper also focuses on typical generic architectures for object detection covering one stage as well as two stage detectors to improve the detection performance further. Multiple approaches to microscopic blood cells examinations are analysed and compared using various performance metrics.