Classification of benign and malignant subtypes of breast cancer histopathology imaging using hybrid CNN-LSTM based transfer learning

Background Grading of cancer histopathology slides requires more pathologists and expert clinicians as well as it is time consuming to look manually into whole-slide images. Hence, an automated classification of histopathological breast cancer sub-type is useful for clinical diagnosis and therapeuti...

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Veröffentlicht in:	BMC medical imaging Jg. 23; H. 1; S. 19 - 15
Hauptverfasser:	Srikantamurthy, Mahati Munikoti, Rallabandi, V. P. Subramanyam, Dudekula, Dawood Babu, Natarajan, Sathishkumar, Park, Junhyung
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 30.01.2023 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Accuracy Algorithms Artificial neural networks Automation Benign Breast cancer Breast Neoplasms - diagnostic imaging Breast Neoplasms - pathology Classification Datasets Deep learning Diagnosis Diagnostic Imaging Female Health aspects Histochemistry Histopathology Humans Image acquisition Image analysis Image classification Image processing Imaging Long short-term memory Machine Learning Mammography Medical imaging Medical imaging equipment Medicine Medicine & Public Health Methods Model accuracy Neural networks Neural Networks, Computer Radiology Recurrent neural networks Support vector machines Transfer learning Deep learning Breast cancer Histopathology Image classification
ISSN:	1471-2342, 1471-2342
Online-Zugang:	Volltext
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