AIMC Topic: Deep Learning

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On Merging Feature Engineering and Deep Learning for Diagnosis, Risk Prediction and Age Estimation Based on the 12-Lead ECG.

IEEE transactions on bio-medical engineering
OBJECTIVE: Over the past few years, deep learning (DL) has been used extensively in research for 12-lead electrocardiogram (ECG) analysis. However, it is unclear whether the explicit or implicit claims made on DL superiority to the more classical fea...

Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy.

Sensors (Basel, Switzerland)
Diabetic retinopathy (DR) is a common complication of long-term diabetes, affecting the human eye and potentially leading to permanent blindness. The early detection of DR is crucial for effective treatment, as symptoms often manifest in later stages...

Enhanced Deep-Learning-Based Automatic Left-Femur Segmentation Scheme with Attribute Augmentation.

Sensors (Basel, Switzerland)
This research proposes augmenting cropped computed tomography (CT) slices with data attributes to enhance the performance of a deep-learning-based automatic left-femur segmentation scheme. The data attribute is the lying position for the left-femur m...

Human Activity Recognition Using Attention-Mechanism-Based Deep Learning Feature Combination.

Sensors (Basel, Switzerland)
Human activity recognition (HAR) performs a vital function in various fields, including healthcare, rehabilitation, elder care, and monitoring. Researchers are using mobile sensor data (i.e., accelerometer, gyroscope) by adapting various machine lear...

Adapting model-based deep learning to multiple acquisition conditions: Ada-MoDL.

Magnetic resonance in medicine
PURPOSE: The aim of this work is to introduce a single model-based deep network that can provide high-quality reconstructions from undersampled parallel MRI data acquired with multiple sequences, acquisition settings, and field strengths.

Denoising single MR spectra by deep learning: Miracle or mirage?

Magnetic resonance in medicine
PURPOSE: The inherently poor SNR of MRS measurements presents a significant hurdle to its clinical application. Denoising by machine or deep learning (DL) was proposed as a remedy. It is investigated whether such denoising leads to lower estimate unc...

Efficient evaluation of photodynamic therapy on tumor based on deep learning.

Photodiagnosis and photodynamic therapy
Photodynamic therapy (PDT) is a non-invasive treatment method for treating tumors. Under laser irradiation, photosensitizers in tumor tissues generate biotoxic reactive oxygen, which can kill tumor cells. The traditional live/dead staining method of ...

Joint Fusion and Detection via Deep Learning in UAV-Borne Multispectral Sensing of Scatterable Landmine.

Sensors (Basel, Switzerland)
Compared with traditional mine detection methods, UAV-based measures are more suitable for the rapid detection of large areas of scatterable landmines, and a multispectral fusion strategy based on a deep learning model is proposed to facilitate mine ...

DDD TinyML: A TinyML-Based Driver Drowsiness Detection Model Using Deep Learning.

Sensors (Basel, Switzerland)
Driver drowsiness is one of the main causes of traffic accidents today. In recent years, driver drowsiness detection has suffered from issues integrating deep learning (DL) with Internet-of-things (IoT) devices due to the limited resources of IoT dev...

Effect of Gait Speed on Trajectory Prediction Using Deep Learning Models for Exoskeleton Applications.

Sensors (Basel, Switzerland)
Gait speed is an important biomechanical determinant of gait patterns, with joint kinematics being influenced by it. This study aims to explore the effectiveness of fully connected neural networks (FCNNs), with a potential application for exoskeleton...