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Found 12 papers, 0 papers with code
DGAFF: Deep Genetic Algorithm Fitness Formation for EEG Bio-Signal Channel Selection
The proposed method accelerates the convergence of the genetic algorithm and increases the system's performance.
Brain Tumor Classification by Cascaded Multiscale Multitask Learning Framework Based on Feature Aggregation
Brain tumor analysis in MRI images is a significant and challenging issue because misdiagnosis can lead to death.
Segmentation of Lungs COVID Infected Regions by Attention Mechanism and Synthetic Data
Hence, infected patients can get better service from the healthcare system and decrease the number of deaths caused by the coronavirus.
Compound Frechet Inception Distance for Quality Assessment of GAN Created Images
Our proposed method integrates three levels of feature abstractions to evaluate the quality of generated images.
Robust Watermarking using Diffusion of Logo into Autoencoder Feature Maps
In this paper, we propose to use an end-to-end network for watermarking.
Bifurcated Autoencoder for Segmentation of COVID-19 Infected Regions in CT Images
We propose a bifurcated 2-D model for two types of segmentation.
Brain Tumor Classification Using Medial Residual Encoder Layers
For brain tumor classification in this work, we offer a system based on deep learning, containing encoder blocks.
Classification of Diabetic Retinopathy Using Unlabeled Data and Knowledge Distillation
Knowledge distillation addresses some of the shortcomings associated with transfer learning by generalizing a complex model to a lighter model.
Selection of Proper EEG Channels for Subject Intention Classification Using Deep Learning
Reduction of the number of channels could reduce the complexity of brain-computer-interface devices.
Fault-Tolerant Routing in Hypercube Networks by Avoiding Faulty Nodes
In this paper, a neural routing method is introduced which is named as Fault Avoidance Routing (FAR).
Segmentation of Bleeding Regions in Wireless Capsule Endoscopy for Detection of Informative Frames
Both CNN and MLP structures are simplified to reduce the number of computational operations.
Flow of Information in Feed-Forward Deep Neural Networks
Feed-forward deep neural networks have been used extensively in various machine learning applications.
