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Found 27 papers, 5 papers with code
A Recent Survey of Vision Transformers for Medical Image Segmentation
This survey paper provides a detailed review of the recent advancements in ViTs and HVTs for medical image segmentation.
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.
MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentation
Recently, Transformers have gained popularity in the computer vision community and also in medical image segmentation due to their ability to process global features effectively.
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Medical Image Segmentation
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Deep Neural Networks based Meta-Learning for Network Intrusion Detection
First, a hybrid feature space is created by integrating decision and feature spaces.
Early Risk Prediction of Chronic Myeloid Leukemia with Protein Sequences using Machine Learning-based Meta-Ensemble
In the study, we developed a multi-layer-perception-based meta-ensemble system using protein amino acid sequences for early risk prediction of CML.
Traffic Management of Autonomous Vehicles using Policy Based Deep Reinforcement Learning and Intelligent Routing
To deal with the congestion on roads behind the intersection, we used re-routing technique to load balance the vehicles on road networks.
Autonomous Drone Swarm Navigation and Multi-target Tracking in 3D Environments with Dynamic Obstacles
An autonomous approach employing deep reinforcement learning is presented in this study for swarm navigation.
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.
IoT Malware Detection Architecture using a Novel Channel Boosted and Squeezed CNN
The proposed architecture exploits the concepts of edge and smoothing, multi-path dilated convolutional operations, channel squeezing, and boosting in CNN.
A New Deep Hybrid Boosted and Ensemble Learning-based Brain Tumor Analysis using MRI
While in the second phase, a new hybrid features fusion-based brain tumor classification approach is proposed, comprised of dynamic-static feature and ML classifier to categorize different tumor types.
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.
Segmentation of Shoulder Muscle MRI Using a New Region and Edge based Deep Auto-Encoder
The performances of the proposed MRI segmentation based DAE architectures have been tested using a 3D MRI shoulder muscle dataset using the hold-out cross-validation technique.
FANCA: In-Silico deleterious mutation analysis for early prediction of leukemia
First, we study individual SNPs in the coding region of FANCA and computational tools like PROVEAN, PolyPhen2, MuPro, and PANTHER to compute deleterious mutation scores.
Malware Classification Using Deep Boosted Learning
In the hybrid learning, Deep features are extracted from customized CNN architectures and fed into the conventional machine learning classifier to improve the classification performance.
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.
Extracting Signals of Higgs Boson From Background Noise Using Deep Neural Networks
Higgs boson is a fundamental particle, and the classification of Higgs signals is a well-known problem in high energy physics.
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.
Wind Speed Prediction using Deep Ensemble Learning with a Jet-like Architecture
Inspired by the shape and working of a jet, a novel Deep Ensemble Learning using Jet-like Architecture (DEL-Jet) technique is proposed to enhance the diversity and robustness of a learning system against the variations in the input space.
Malware Classification using Deep Learning based Feature Extraction and Wrapper based Feature Selection Technique
The proposed DLMD technique uses both the byte and ASM files for feature engineering, thus classifying malware families.
A Recent Survey on the Applications of Genetic Programming in Image Processing
GP has thus been used in different ways for Image Processing since its inception.
Transfer Learning and Meta Classification Based Deep Churn Prediction System for Telecom Industry
However, the development of a churn prediction system for a telecom industry is a challenging task, mainly due to the large size of the data, high dimensional features, and imbalanced distribution of the data.
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.
Adaptive Transfer Learning in Deep Neural Networks: Wind Power Prediction using Knowledge Transfer from Region to Region and Between Different Task Domains
This paper introduces the idea of Adaptive Transfer Learning in Deep Neural Networks (ATL-DNN) for wind power prediction.
Deep Belief Networks Based Feature Generation and Regression for Predicting Wind Power
It is experimentally shown that the deep learning and unsupervised pre-training capabilities of DBN based model has comparable and in some cases better results than hybrid and complex learning techniques proposed for wind power prediction.
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.
