Semantic Segmentation of Anaemic RBCs Using Multilevel Deep Convolutional Encoder-Decoder Network

Pixel-level analysis of blood images plays a pivotal role in diagnosing blood-related diseases, especially Anaemia. These analyses mainly rely on an accurate diagnosis of morphological deformities like shape, size, and precise pixel counting. In traditional segmentation approaches, instance or objec...

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Veröffentlicht in:	IEEE access Jg. 9; S. 161326 - 161341
Hauptverfasser:	Shahzad, Muhammad, Umar, Arif Iqbal, Shirazi, Syed Hamad, Shaikh, Israr Ahmed
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Anemia Artificial neural networks Blood blood cells Cells (biology) CNN Coders Computer architecture Convolutional neural networks Datasets deep learning Encoders-Decoders Erythrocytes Image segmentation Medical imaging Morphology multi-level deep convolutional encoder-decoder network Performance evaluation Pixels segmentation analysis Semantic segmentation Semantics Shape Training
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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