no code implementations • 19 Jan 2024 • Md Zahangir Alom, Quynh T. Tran, Brent A. Orr
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.
no code implementations • 5 May 2021 • Dhaval D. Kadia, Md Zahangir Alom, Ranga Burada, Tam V. Nguyen, Vijayan K. Asari
In particular, the proposed model integrates 3D convolution into the Recurrent Residual Neural Network based on U-Net.
no code implementations • 5 Jul 2020 • Md Zahangir Alom, Raj P. Kapur, TJ Browen, Vijayan K. Asari
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.
no code implementations • 7 Apr 2020 • Md Zahangir Alom, M M Shaifur Rahman, Mst Shamima Nasrin, Tarek M. Taha, Vijayan K. Asari
In this paper, we propose a fast and efficient way to identify COVID-19 patients with multi-task deep learning (DL) methods.
1 code implementation • 25 Apr 2019 • Md Zahangir Alom, Theus Aspiras, Tarek M. Taha, Vijayan K. Asari
Several DL architectures have been proposed for classification, segmentation, and detection tasks in medical imaging and computational pathology.
no code implementations • 19 Apr 2019 • Md Zahangir Alom, Theus Aspiras, Tarek M. Taha, Vijayan K. Asari, TJ Bowen, Dave Billiter, Simon Arkell
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).
1 code implementation • 10 Nov 2018 • Md Zahangir Alom, Chris Yakopcic, Tarek M. Taha, Vijayan K. Asari
The Deep Convolutional Neural Network (DCNN) is one of the most powerful and successful deep learning approaches.
no code implementations • 8 Nov 2018 • Md Zahangir Alom, Chris Yakopcic, Tarek M. Taha, Vijayan K. Asari
The experimental results show that the proposed DCNN models provide superior performance compared to the existing approaches for nuclei classification, segmentation, and detection tasks.
no code implementations • 4 Sep 2018 • M M Shaifur Rahman, Mst Shamima Nasrin, Moin Mostakim, Md Zahangir Alom
None of them are used to deploy a physical system for Bangla License Plate Recognition System (BLPRS) due to their poor recognition accuracy.
no code implementations • 3 Mar 2018 • Md Zahangir Alom, Tarek M. Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Brian C Van Esesn, Abdul A S. Awwal, Vijayan K. Asari
Furthermore, DL approaches have explored and evaluated in different application domains are also included in this survey.
12 code implementations • 20 Feb 2018 • Md Zahangir Alom, Mahmudul Hasan, Chris Yakopcic, Tarek M. Taha, Vijayan K. Asari
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.
no code implementations • 7 Feb 2018 • Md Zahangir Alom, Theodore Josue, Md Nayim Rahman, Will Mitchell, Chris Yakopcic, Tarek M. Taha
IBM's 2016 release of a deep learning framework for DCNNs, called Energy Efficient Deep Neuromorphic Networks (Eedn).
no code implementations • 7 Feb 2018 • Md Zahangir Alom, Adam T Moody, Naoya Maruyama, Brian C. Van Essen, Tarek M. Taha
These proposed approaches are evaluated on different datasets for sentiment analysis on IMDB and video frame predictions on the moving MNIST dataset.
1 code implementation • 28 Dec 2017 • Md Zahangir Alom, Peheding Sidike, Mahmudul Hasan, Tark M. Taha, Vijayan K. Asari
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.
no code implementations • 28 Dec 2017 • Md Zahangir Alom, Mahmudul Hasan, Chris Yakopcic, Tarek M. Taha, Vijayan K. Asari
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.
no code implementations • 7 May 2017 • Md Zahangir Alom, Paheding Sidike, Tarek M. Taha, Vijayan K. Asari
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.
1 code implementation • CVPR 2015 • Md Zahangir Alom, Mahmudul Hasan, Chris Yakopcic, Tarek M. Taha
Furthermore, we have investigated IRCNN performance against equivalent Inception Networks and Inception-Residual Networks using the CIFAR-100 dataset.
no code implementations • 21 Apr 2017 • Md Zahangir Alom, Tarek M. Taha
The proposed system consists of two phases: multi-view pedestrian detection and tracking.