PolypSegNet: A modified encoder-decoder architecture for automated polyp segmentation from colonoscopy images

Colorectal cancer has become one of the major causes of death throughout the world. Early detection of Polyp, an early symptom of colorectal cancer, can increase the survival rate to 90%. Segmentation of Polyp regions from colonoscopy images can facilitate the faster diagnosis. Due to varying sizes,...

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Veröffentlicht in:	Computers in biology and medicine Jg. 128; S. 104119
Hauptverfasser:	Mahmud, Tanvir, Paul, Bishmoy, Fattah, Shaikh Anowarul
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 01.01.2021 Elsevier Limited
Schlagworte:	Architecture Artificial neural networks Automation Cancer Coders Colonoscopy Colorectal cancer Colorectal carcinoma Computer architecture Computer-aided diagnosis Diagnosis Diagnostic systems Encoders-Decoders Feature maps Image processing Image segmentation Internal Medicine Medical diagnosis Medical imaging Modules Neural network Neural networks Other Polyp segmentation Polyps Reconstruction Semantics Neural network Colonoscopy Polyp segmentation Computer-aided diagnosis Colorectal cancer colorectal cancer computer-aided diagnosis colonoscopy neural network
ISSN:	0010-4825, 1879-0534, 1879-0534
Online-Zugang:	Volltext
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