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Found 10 papers, 3 papers with code
A survey of the Vision Transformers and its CNN-Transformer based Variants
This survey presents a taxonomy of the recent vision transformer architectures and more specifically that of the hybrid vision transformers.
CB-HVTNet: A channel-boosted hybrid vision transformer network for lymphocyte assessment in histopathological images
To address this issue, we propose a Channel Boosted Hybrid Vision Transformer (CB HVT) that uses transfer learning to generate boosted channels and employs both transformers and CNNs to analyse lymphocytes in histopathological images.
Deep Neural Networks based Meta-Learning for Network Intrusion Detection
First, a hybrid feature space is created by integrating decision and feature spaces.
A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron
The Coronavirus (COVID-19) outbreak in December 2019 has become an ongoing threat to humans worldwide, creating a health crisis that infected millions of lives, as well as devastating the global economy.
Detection of Tumour Infiltrating Lymphocytes in CD3 and CD8 Stained Histopathological Images using a Two-Phase Deep CNN
Results: The empirical evaluation on samples from LYSTO dataset shows that the proposed LSTAM-Net can learn variations in the images and precisely remove the hard negative stain artifacts with an F-score of 0. 74.
COVID-19 Detection in Chest X-Ray Images using a New Channel Boosted CNN
In this work, a new classification technique CB-STM-RENet based on deep Convolutional Neural Network (CNN) and Channel Boosting is proposed for the screening of COVID-19 in chest X-Rays.
Classification and Region Analysis of COVID-19 Infection using Lung CT Images and Deep Convolutional Neural Networks
In the second stage, the CT images classified as infectious images are provided to the segmentation models for the identification and analysis of COVID-19 infectious regions.
Deep Object Detection based Mitosis Analysis in Breast Cancer Histopathological Images
These challenges undermine the precision of the automated detection model and thus make detection difficult in a single phase.
A Survey of the Recent Architectures of Deep Convolutional Neural Networks
The availability of a large amount of data and improvement in the hardware technology has accelerated the research in CNNs, and recently interesting deep CNN architectures have been reported.
A New Channel Boosted Convolutional Neural Network using Transfer Learning
In the proposed methodology, a deep CNN is boosted by various channels available through TL from already trained Deep Neural Networks, in addition to its original channel.
