Deep transfer learning with improved crayfish optimization algorithm for oral squamous cell carcinoma cancer recognition using histopathological images

Oral Squamous Cell Carcinoma (OSCC) causes a severe challenge in oncology due to the lack of diagnostic devices, leading to delays in detecting the disorder. The OSCC diagnosis through histopathology demands a pathologist expert because the cellular presentation is variable and highly complex. Exist...

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Veröffentlicht in:	Scientific reports Jg. 14; H. 1; S. 25348 - 22
Hauptverfasser:	Ragab, Mahmoud, Asar, Turky Omar
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 25.10.2024 Nature Publishing Group Nature Portfolio
Schlagworte:	639/705/117 639/705/258 Accuracy Algorithms Animals Bidirectional long short-term memory Bilateral filtering Cancer Carcinoma, Squamous Cell - diagnosis Carcinoma, Squamous Cell - pathology Crayfish optimization algorithm Deep Learning Diagnosis Histopathological image Histopathology Humanities and Social Sciences Humans Image processing Image Processing, Computer-Assisted - methods Long short-term memory Mouth Neoplasms - diagnosis Mouth Neoplasms - pathology multidisciplinary Neural networks Neural Networks, Computer Optimization algorithms Oral cancer Oral carcinoma Oral squamous cell carcinoma Science Science (multidisciplinary) Squamous cell carcinoma Squeeze-excitation- CapsNet Survival Transfer learning Histopathological image Oral cancer Bidirectional long short-term memory Bilateral filtering Crayfish optimization algorithm Squeeze-excitation- CapsNet
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
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