Search Results for author:
Found 14 papers, 4 papers with code
COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches
In this paper, we propose a fast and efficient way to identify COVID-19 patients with multi-task deep learning (DL) methods.
High Speed Cognitive Domain Ontologies for Asset Allocation Using Loihi Spiking Neurons
In this work we show that a grid of isolated spiking neurons is capable of generating solutions to this problem very quickly, although some degree of approximation is required to achieve the speedup.
Skin Cancer Segmentation and Classification with NABLA-N and Inception Recurrent Residual Convolutional Networks
Several DL architectures have been proposed for classification, segmentation, and detection tasks in medical imaging and computational pathology.
Advanced Deep Convolutional Neural Network Approaches for Digital Pathology Image Analysis: a comprehensive evaluation with different use cases
Deep Learning (DL) approaches have been providing state-of-the-art performance in different modalities in the field of medical imagining including Digital Pathology Image Analysis (DPIA).
Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network
The Deep Convolutional Neural Network (DCNN) is one of the most powerful and successful deep learning approaches.
Microscopic Nuclei Classification, Segmentation and Detection with improved Deep Convolutional Neural Network (DCNN) Approaches
The experimental results show that the proposed DCNN models provide superior performance compared to the existing approaches for nuclei classification, segmentation, and detection tasks.
The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches
Furthermore, DL approaches have explored and evaluated in different application domains are also included in this survey.
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
In this paper, we propose a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net models, which are named RU-Net and R2U-Net respectively.
Effective Quantization Approaches for Recurrent Neural Networks
These proposed approaches are evaluated on different datasets for sentiment analysis on IMDB and video frame predictions on the moving MNIST dataset.
Deep Versus Wide Convolutional Neural Networks for Object Recognition on Neuromorphic System
IBM's 2016 release of a deep learning framework for DCNNs, called Energy Efficient Deep Neuromorphic Networks (Eedn).
Improved Inception-Residual Convolutional Neural Network for Object Recognition
In this paper, we introduce a new DCNN model called the Inception Recurrent Residual Convolutional Neural Network (IRRCNN), which utilizes the power of the Recurrent Convolutional Neural Network (RCNN), the Inception network, and the Residual network.
Handwritten Bangla Digit Recognition Using Deep Learning
To improve the performance of Handwritten Bangla Digit Recognition (HBDR), we herein present a new approach based on deep neural networks which have recently shown excellent performance in many pattern recognition and machine learning applications, but has not been throughly attempted for HBDR.
Inception Recurrent Convolutional Neural Network for Object Recognition
Furthermore, we have investigated IRCNN performance against equivalent Inception Networks and Inception-Residual Networks using the CIFAR-100 dataset.
Robust Multi-view Pedestrian Tracking Using Neural Networks
The proposed system consists of two phases: multi-view pedestrian detection and tracking.
