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Found 38 papers, 5 papers with code
Machine-agnostic Automated Lumbar MRI Segmentation using a Cascaded Model Based on Generative Neurons
Automated lumbar spine segmentation is very crucial for modern diagnosis systems.
Self-DenseMobileNet: A Robust Framework for Lung Nodule Classification using Self-ONN and Stacking-based Meta-Classifier
In this study, we propose a novel and robust framework, Self-DenseMobileNet, designed to enhance the classification of nodules and non-nodules in chest radiographs (CXRs).
Artificial Intelligence for Cochlear Implants: Review of Strategies, Challenges, and Perspectives
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 partial or profound hearing impairments.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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A Novel 1D Generative Adversarial Network-based Framework for Atrial Fibrillation Detection using Restored Wrist Photoplethysmography Signals
Self-AFNet managed to achieve an accuracy of 98. 07% and 98. 97%, respectively using two ECG splits which is comparable to the performance of AF detection utilizing reconstructed PPG segments.
Atrial Fibrillation Detection
Generative Adversarial Network
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Deep learning in computed tomography pulmonary angiography imaging: a dual-pronged approach for pulmonary embolism detection
The increasing reliance on Computed Tomography Pulmonary Angiography (CTPA) for Pulmonary Embolism (PE) diagnosis presents challenges and a pressing need for improved diagnostic solutions.
A Novel Deep Learning Technique for Morphology Preserved Fetal ECG Extraction from Mother ECG using 1D-CycleGAN
It can also accurately produce the estimation of fetal heart rate and R-R interval with an error of 0. 25% and 0. 27%, respectively.
AI-Driven Personalised Offloading Device Prescriptions: A Cutting-Edge Approach to Preventing Diabetes-Related Plantar Forefoot Ulcers and Complications
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.
Deep Learning based Automatic Quantification of Urethral Plate Quality using the Plate Objective Scoring Tool (POST)
These predicted landmarks are then used to assess UP quality in distal hypospadias.
Design, Implementation, and Performance Evaluation of a Fiber Bragg Gratings(FBG) based Smart Insole to Measure Plantar Pressure and Temperature
Various foot complications can be easily avoided by continuous monitoring of foot plantar temperature and pressure.
BIO-CXRNET: A Robust Multimodal Stacking Machine Learning Technique for Mortality Risk Prediction of COVID-19 Patients using Chest X-Ray Images and Clinical Data
This study uses 25 biomarkers and CXR images in predicting the risk in 930 COVID-19 patients admitted during the first wave of COVID-19 (March-June 2020) in Italy.
Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis
The highest average {\Delta}SNR and {\eta} using single-stage artifacts removal techniques (WPD) are found as 16. 11 dB and 26. 40%, respectively for all the fNIRS signals using fk4 wavelet packet.
A machine learning-based severity prediction tool for diabetic sensorimotor polyneuropathy using Michigan neuropathy screening instrumentations
A multivariable logistic regression-based nomogram was generated and validated for DSPN severity grading.
Novel techniques for improving NNetEn entropy calculation for short and noisy time series
Firstly, this work investigates different methods of filling the reservoir with time series (signal) elements.
OSegNet: Operational Segmentation Network for COVID-19 Detection using Chest X-ray Images
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.
RamanNet: A generalized neural network architecture for Raman Spectrum Analysis
Raman spectroscopy provides a vibrational profile of the molecules and thus can be used to uniquely identify different kind of materials.
A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals
Most existing methods used in the hospitals for continuous monitoring of BP are invasive.
A Machine Learning Model for Early Detection of Diabetic Foot using Thermogram Images
Several studies have reported that thermogram images may help to detect an increase in plantar temperature prior to DFU.
COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network
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).
QUCoughScope: An Artificially Intelligent Mobile Application to Detect Asymptomatic COVID-19 Patients using Cough and Breathing Sounds
Test subjects can simply download a mobile application, enter their symptoms, record an audio clip of their cough and breath, and upload the data anonymously to our servers.
COVID-19 Infection Localization and Severity Grading from Chest X-ray Images
In this study, we address this urgent need by proposing a systematic and unified approach for lung segmentation and COVID-19 localization with infection quantification from CXR images.
A Novel Non-Invasive Estimation of Respiration Rate from Photoplethysmograph Signal Using Machine Learning Model
This ML model based RR estimation can be embedded in wearable devices for real-time continuous monitoring of the patient.
EDITH :ECG biometrics aided by Deep learning for reliable Individual auTHentication
In this work, we present, EDITH, a deep learning-based framework for ECG biometrics authentication system.
Detection and severity classification of COVID-19 in CT images using deep learning
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.
Reliable COVID-19 Detection Using Chest X-ray Images
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.
Robust R-Peak Detection in Low-Quality Holter ECGs using 1D Convolutional Neural Network
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.
Deep Learning Based Classification of Unsegmented Phonocardiogram Spectrograms Leveraging Transfer Learning
Finally, the third study shows a precision of 98. 29% on the noisy PASCAL dataset with transfer learning approach.
Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images
The accuracy, precision, sensitivity, f1-score, and specificity in the detection of COVID-19 with gamma correction on CXR images were 96. 29%, 96. 28%, 96. 29%, 96. 28% and 96. 27% respectively.
COVID-19 Infection Map Generation and Detection from Chest X-Ray Images
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.
An early warning tool for predicting mortality risk of COVID-19 patients using machine learning
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.
Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization
The accuracy, precision, sensitivity, F1-score, specificity in the detection of tuberculosis using X-ray images were 97. 07 %, 97. 34 %, 97. 07 %, 97. 14 % and 97. 36 % respectively.
Advance Warning Methodologies for COVID-19 using Chest X-Ray Images
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.
Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-Ray Images
All networks showed high COVID-19 detection sensitivity (>96%) with the segmented lung images.
Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images
Any technological tool that can be provided to healthcare practitioners to save time, effort, and possibly lives has crucial importance.
Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques
Hypertension is a potentially unsafe health ailment, which can be indicated directly from the Blood pressure (BP).
PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms using Fully Convolutional Neural Networks
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
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.
Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray
The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial and viral pneumonia were 98%, 95%, and 93. 3% respectively.
Can AI help in screening Viral and COVID-19 pneumonia?
The networks were trained to classify two different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and COVID-19 pneumonia with and without image augmentation.
