Retracted: Investigation of Deep Learning Schemes in Medical Application
- 1 December 2019
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 87-92
Abstract
Deep learning models are equipped for thinking out how to concentrate on the correct features without anyone else's input, requiring a little direction from the software engineer. Essentially, deep learning mirrors how our brain is functioning to take decisions. Deep learning techniques are highly applied in medical imaging diagnosis. Deep learning techniques are used in medical applications in four different areas i. Detections ii. Classifications iii. Segmentations iv. Registrations. In this paper we have discussed deep learn scheme advantage, dataset, software and hardware used in medical applications. Further, we discussed the comparative analysis of medical application using deep learning techniques.Keywords
This publication has 25 references indexed in Scilit:
- Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentationMedical Image Analysis, 2017
- Computational mammography using deep neural networksComputer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2016
- Breast cancer classification using deep belief networksExpert Systems with Applications, 2016
- A CNN Regression Approach for Real-Time 2D/3D RegistrationIEEE Transactions on Medical Imaging, 2016
- Glaucoma detection based on deep convolutional neural network2015
- Brain tumor grading based on Neural Networks and Convolutional Neural Networks2015
- Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology ImagesIEEE Transactions on Medical Imaging, 2015
- Automatic Feature Learning to Grade Nuclear Cataracts Based on Deep LearningIEEE Transactions on Biomedical Engineering, 2015
- Lung Nodule Classification Using Deep Features in CT Images2015
- Self-supervised learning model for skin cancer diagnosis2015