AI Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

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Obstructive Sleep Apnea Detection Scheme Based on Manually Generated Features and Parallel Heterogeneous Deep Learning Model Under IoMT.

IEEE journal of biomedical and health informatics
Obstructive sleep apnea (OSA) syndrome is a common sleep disorder and a key cause of cardiovascular and cerebrovascular diseases that seriously affect the lives and health of people. The development of Internet of Medical Things (IoMT) has enabled th...

Hybrid Intelligence-Driven Medical Image Recognition for Remote Patient Diagnosis in Internet of Medical Things.

IEEE journal of biomedical and health informatics
In ear of smart cities, intelligent medical image recognition technique has become a promising way to solve remote patient diagnosis in IoMT. Although deep learning-based recognition approaches have received great development during the past decade, ...

Skeleton-Based Abnormal Behavior Detection Using Secure Partitioned Convolutional Neural Network Model.

IEEE journal of biomedical and health informatics
Theabnormal behavior detection is the vital for evaluation of daily-life health status of the patient with cognitive impairment. Previous studies about abnormal behavior detection indicate that convolution neural network (CNN)-based computer vision o...

DeepCAN: A Modular Deep Learning System for Automated Cell Counting and Viability Analysis.

IEEE journal of biomedical and health informatics
Precise and quick monitoring of key cytometric features such as cell count, size, morphology, and DNA content is crucial in life science applications. Traditionally, image cytometry relies on visual inspection of hemocytometers. This approach is erro...

Using Machine Learning for Predicting the Effect of Mutations in the Initiation Codon.

IEEE journal of biomedical and health informatics
The effect of mutations has been traditionally predicted by studying what may happen due to the substitution of one amino acid for another one. This approach may be effective for mutations with impact in the function of the protein, but ineffective f...

Customized Federated Learning for Multi-Source Decentralized Medical Image Classification.

IEEE journal of biomedical and health informatics
The performance of deep networks for medical image analysis is often constrained by limited medical data, which is privacy-sensitive. Federated learning (FL) alleviates the constraint by allowing different institutions to collaboratively train a fede...

MFL-Net: An Efficient Lightweight Multi-Scale Feature Learning CNN for COVID-19 Diagnosis From CT Images.

IEEE journal of biomedical and health informatics
Timely and accurate diagnosis of coronavirus disease 2019 (COVID-19) is crucial in curbing its spread. Slow testing results of reverse transcription-polymerase chain reaction (RT-PCR) and a shortage of test kits have led to consider chest computed to...

Anatomy-XNet: An Anatomy Aware Convolutional Neural Network for Thoracic Disease Classification in Chest X-Rays.

IEEE journal of biomedical and health informatics
Thoracic disease detection from chest radiographs using deep learning methods has been an active area of research in the last decade. Most previous methods attempt to focus on the diseased organs of the image by identifying spatial regions responsibl...

Data-Driven Guided Attention for Analysis of Physiological Waveforms With Deep Learning.

IEEE journal of biomedical and health informatics
Estimating physiological parameters - such as blood pressure (BP) - from raw sensor data captured by noninvasive, wearable devices rely on either burdensome manual feature extraction designed by domain experts to identify key waveform characteristics...

Continuous Estimation of Human Joint Angles From sEMG Using a Multi-Feature Temporal Convolutional Attention-Based Network.

IEEE journal of biomedical and health informatics
Intention recognition based on surface electromyography (sEMG) signals is pivotal in human-machine interaction (HMI), where continuous motion estimation with high accuracy has been the challenge. The convolutional neural network (CNN) possesses excel...