Search Results for author: Muhammad E. H. Chowdhury

Found 36 papers, 4 papers with code

Advanced Artificial Intelligence Algorithms in Cochlear Implants: Review of Healthcare Strategies, Challenges, and Perspectives

no code implementations17 Mar 2024 Billel Essaid, Hamza Kheddar, Noureddine Batel, Abderrahmane Lakas, Muhammad E. H. Chowdhury

Automatic speech recognition (ASR) plays a pivotal role in our daily lives, offering utility not only for interacting with machines but also for facilitating communication for individuals with either partial or profound hearing impairments.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

AI-Driven Personalised Offloading Device Prescriptions: A Cutting-Edge Approach to Preventing Diabetes-Related Plantar Forefoot Ulcers and Complications

no code implementations6 Sep 2023 Sayed Ahmed, Muhammad Ashad Kabir, Muhammad E. H. Chowdhury, Susan Nancarrow

This chapter proposes an AI-powered Clinical Decision Support System (CDSS) to recommend personalised prescriptions of offloading devices (footwear and insoles) for patients with diabetes who are at risk of foot complications.

OSegNet: Operational Segmentation Network for COVID-19 Detection using Chest X-ray Images

no code implementations21 Feb 2022 Aysen Degerli, Serkan Kiranyaz, Muhammad E. H. Chowdhury, Moncef Gabbouj

To address the data scarcity encountered in training and especially in evaluation, this study extends the largest COVID-19 CXR dataset: QaTa-COV19 with 121, 378 CXRs including 9258 COVID-19 samples with their corresponding ground-truth segmentation masks that are publicly shared with the research community.

Segmentation

RamanNet: A generalized neural network architecture for Raman Spectrum Analysis

1 code implementation20 Jan 2022 Nabil Ibtehaz, Muhammad E. H. Chowdhury, Amith Khandakar, Susu M. Zughaier, Serkan Kiranyaz, M. Sohel Rahman

Raman spectroscopy provides a vibrational profile of the molecules and thus can be used to uniquely identify different kind of materials.

BIG-bench Machine Learning Virology

COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network

no code implementations1 Jun 2021 Tawsifur Rahman, Alex Akinbi, Muhammad E. H. Chowdhury, Tarik A. Rashid, Abdulkadir Şengür, Amith Khandakar, Khandaker Reajul Islam, Aras M. Ismael

Six different deep CNN models (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and MobileNetv2) were used to investigate three different classification schemes: two-class classification (Normal vs COVID-19); three-class classification (Normal, COVID-19, and Other CVDs), and finally, five-class classification (Normal, COVID-19, MI, AHB, and RMI).

Classification

Detection and severity classification of COVID-19 in CT images using deep learning

no code implementations15 Feb 2021 Yazan Qiblawey, Anas Tahir, Muhammad E. H. Chowdhury, Amith Khandakar, Serkan Kiranyaz, Tawsifur Rahman, Nabil Ibtehaz, Sakib Mahmud, Somaya Al-Madeed, Farayi Musharavati

Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94. 13% and IoU of 91. 85% using the FPN model with the DenseNet201 encoder.

Computed Tomography (CT) General Classification +1

Reliable COVID-19 Detection Using Chest X-ray Images

no code implementations28 Jan 2021 Aysen Degerli, Mete Ahishali, Serkan Kiranyaz, Muhammad E. H. Chowdhury, Moncef Gabbouj

To address this need, in this study, we propose a reliable COVID-19 detection network: ReCovNet, which can discriminate COVID-19 pneumonia from 14 different thoracic diseases and healthy subjects.

COVID-19 Diagnosis Specificity

Robust R-Peak Detection in Low-Quality Holter ECGs using 1D Convolutional Neural Network

no code implementations29 Dec 2020 Muhammad Uzair Zahid, Serkan Kiranyaz, Turker Ince, Ozer Can Devecioglu, Muhammad E. H. Chowdhury, Amith Khandakar, Anas Tahir, Moncef Gabbouj

Results also demonstrate similar or better performance than most competing algorithms on MIT-DB with 99. 83% F1-score, 99. 85% recall, and 99. 82% precision.

COVID-19 Infection Map Generation and Detection from Chest X-Ray Images

no code implementations26 Sep 2020 Aysen Degerli, Mete Ahishali, Mehmet Yamac, Serkan Kiranyaz, Muhammad E. H. Chowdhury, Khalid Hameed, Tahir Hamid, Rashid Mazhar, Moncef Gabbouj

To accomplish this, we have compiled the largest dataset with 119, 316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human-machine approach.

COVID-19 Diagnosis Segmentation +1

An early warning tool for predicting mortality risk of COVID-19 patients using machine learning

no code implementations29 Jul 2020 Muhammad E. H. Chowdhury, Tawsifur Rahman, Amith Khandakar, Somaya Al-Madeed, Susu M. Zughaier, Suhail A. R. Doi, Hanadi Hassen, Mohammad T. Islam

The prognostic model, nomogram and LNLCA score can help in early detection of high mortality risk of COVID-19 patients, which will help doctors to improve the management of patient stratification.

BIG-bench Machine Learning Management

Advance Warning Methodologies for COVID-19 using Chest X-Ray Images

1 code implementation7 Jun 2020 Mete Ahishali, Aysen Degerli, Mehmet Yamac, Serkan Kiranyaz, Muhammad E. H. Chowdhury, Khalid Hameed, Tahir Hamid, Rashid Mazhar, Moncef Gabbouj

The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage.

Specificity Transfer Learning

PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms using Fully Convolutional Neural Networks

1 code implementation4 May 2020 Nabil Ibtehaz, Sakib Mahmud, Muhammad E. H. Chowdhury, Amith Khandakar, Mohamed Arselene Ayari, Anas Tahir, M. Sohel Rahman

This motivates us to develop a method to predict the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using photoplethysmogram (PPG) signals.

An Intelligent and Low-cost Eye-tracking System for Motorized Wheelchair Control

no code implementations2 May 2020 Mahmoud Dahmani, Muhammad E. H. Chowdhury, Amith Khandakar, Tawsifur Rahman, Khaled Al-Jayyousi, Abdalla Hefny, Serkan Kiranyaz

In the 34 developed and 156 developing countries, there are about 132 million disabled people who need a wheelchair constituting 1. 86% of the world population.

Gaze Estimation Template Matching

Cannot find the paper you are looking for? You can Submit a new open access paper.