A hybrid end-to-end learning approach for breast cancer diagnosis: convolutional recurrent network
- 28 December 2022
- journal article
- research article
- Published by Elsevier in Computers and Electrical Engineering
- Vol. 105
Abstract
No abstract availableKeywords
This publication has 39 references indexed in Scilit:
- Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosisScientific Reports, 2016
- Breast mass classification in digital mammography based on extreme learning machineNeurocomputing, 2016
- A Dataset for Breast Cancer Histopathological Image ClassificationIEEE Transactions on Biomedical Engineering, 2015
- Benign and malignant breast tumors classification based on region growing and CNN segmentationExpert Systems with Applications, 2015
- Mammogram Classification using Law's Texture Energy Measure and Neural NetworksProcedia Computer Science, 2015
- Topological Modeling and Classification of Mammographic Microcalcification ClustersIEEE Transactions on Biomedical Engineering, 2014
- INbreastAcademic Radiology, 2012
- Automated assessment of breast tissue density in digital mammogramsComputer Vision and Image Understanding, 2010
- Framewise phoneme classification with bidirectional LSTM and other neural network architecturesNeural Networks, 2005
- Long Short-Term MemoryNeural Computation, 1997