Search Results for author:
Found 18 papers, 5 papers with code
Learned Image resizing with efficient training (LRET) facilitates improved performance of large-scale digital histopathology image classification models
Our method, termed Learned Resizing with Efficient Training (LRET), couples efficient training techniques with image resizing to facilitate seamless integration of larger histology image patches into state-of-the-art classification models while preserving important structural information.
R2U3D: Recurrent Residual 3D U-Net for Lung Segmentation
In particular, the proposed model integrates 3D convolution into the Recurrent Residual Neural Network based on U-Net.
GanglionNet: Objectively Assess the Density and Distribution of Ganglion Cells With NABLA-N Network
The proposed method shows a robust 97. 49% detection accuracy for ganglion cells, when compared to counts by the expert pathologist, which demonstrates the robustness of GanglionNet.
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.
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.
Bangla License Plate Recognition Using Convolutional Neural Networks (CNN)
None of them are used to deploy a physical system for Bangla License Plate Recognition System (BLPRS) due to their poor recognition accuracy.
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).
Handwritten Bangla Character Recognition Using The State-of-Art Deep Convolutional Neural Networks
In spite of advances in object recognition technology, Handwritten Bangla Character Recognition (HBCR) remains largely unsolved due to the presence of many ambiguous handwritten characters and excessively cursive Bangla handwritings.
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.
