2021
DOI: 10.1007/s11227-021-04125-4
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Automated segmentation of leukocyte from hematological images—a study using various CNN schemes

Abstract: Medical images play a fundamental role in disease screening, and automated evaluation of these images is widely preferred in hospitals. Recently, Convolutional Neural Network (CNN) supported medical data assessment is widely adopted to inspect a set of medical imaging modalities. Extraction of the leukocyte section from a thin blood smear image is one of the essential procedures during the preliminary disease screening process. The conventional segmentation needs complex/hybrid procedures to extract the necess… Show more

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Cited by 46 publications
(35 citation statements)
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“…In the context of segmentation of infections and lesions, very promising deep learning systems have been suggested for medical image analysis [32][33][34][35][36][37]. Mostly, this work is dedicated to segmenting the lungs and the classification of regions of infection to support clinical evaluation and diagnosis [38].…”
Section: Related Workmentioning
confidence: 99%
“…In the context of segmentation of infections and lesions, very promising deep learning systems have been suggested for medical image analysis [32][33][34][35][36][37]. Mostly, this work is dedicated to segmenting the lungs and the classification of regions of infection to support clinical evaluation and diagnosis [38].…”
Section: Related Workmentioning
confidence: 99%
“…proposed an improvement of the mask RCNN by adding and adjusting hyper‐parameters and spatial information. The leukocyte segmentation step can also be considered as part of a cell recognition process; as in Matek et al 20 article, which uses the ResNeXt CNN architecture for single cells enumeration 21 . published a study with multiple CNN schemes (SegNet and VGG‐U‐Net) for automated segmentation of hematological images.…”
Section: Related Workmentioning
confidence: 99%
“…Recently, deep learning showed a huge improvement for cell segmentation [ 27 ], skin melanoma detection [ 28 ], hemorrhage detection [ 29 ], and a few more [ 30 , 31 ]. In medical imaging, deep learning was successful, especially for breast cancer [ 32 ], COVID-19 [ 33 ], Alzheimer’s disease recognition [ 34 ], brain tumor [ 35 ] diagnostics, and more [ 36 , 37 , 38 ].…”
Section: Introductionmentioning
confidence: 99%