Search Results for author:
Found 7 papers, 2 papers with code
MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification
It exploits the high sensitivity to the multi-level contextual information using an uncertainty quantification component to accomplish a novel dynamic ensemble model. MCUamodelhas achieved a high accuracy of 98. 11% on a breast cancer histology image dataset.
Breast Cancer Histology Image Classification
Classification
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4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection
We used 50, 000 unlabelled chest X-ray images to achieve our coarse-to-fine transfer learning with an application to COVID-19 detection, as an exemplar.
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (CNNs) for image recognition and classification.
An Automatic Seeded Region Growing for 2D Biomedical Image Segmentation
Second, the initial seeds are automatically selected based on ROIs extracted from the image.
Unsupervised Parallel Extraction based Texture for Efficient Image Representation
SOM is a type of unsupervised learning where the goal is to discover some underlying structure of the data.
An Enhancement Neighborhood connected Segmentation for 2D-Cellular Image
Second, the initial seeds are automatically selected based on ROIs extracted from the image.
Self Organization Map based Texture Feature Extraction for Efficient Medical Image Categorization
In this approach, we apply three different partitioning approaches as a region of interested (ROI) selection methods for extracting different accurate textural features from medical image as a primary step of our extraction method.
