AIMC Topic: Deep Learning

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Analysis of Deep Learning-Based Phase Retrieval Algorithm Performance for Quantitative Phase Imaging Microscopy.

Sensors (Basel, Switzerland)
Quantitative phase imaging has been of interest to the science and engineering community and has been applied in multiple research fields and applications. Recently, the data-driven approach of artificial intelligence has been utilized in several opt...

Automatic segmentation model of intercondylar fossa based on deep learning: a novel and effective assessment method for the notch volume.

BMC musculoskeletal disorders
BACKGROUND: Notch volume is associated with anterior cruciate ligament (ACL) injury. Manual tracking of intercondylar notch on MR images is time-consuming and laborious. Deep learning has become a powerful tool for processing medical images. This stu...

Split BiRNN for real-time activity recognition using radar and deep learning.

Scientific reports
Radar systems can be used to perform human activity recognition in a privacy preserving manner. This can be achieved by using Deep Neural Networks, which are able to effectively process the complex radar data. Often these networks are large and do no...

Integrated Blockchain-Deep Learning Approach for Analyzing the Electronic Health Records Recommender System.

Frontiers in public health
Blockchain is a recent revolutionary technology primarily associated with cryptocurrencies. It has many unique features including its acting as a decentralized, immutable, shared, and distributed ledger. Blockchain can store all types of data with be...

The Blockchain Technology Applied in the Development of Real Economy in Jiangsu under Deep Learning.

Computational intelligence and neuroscience
This study focuses on the financing difficulties of small and medium enterprises (SMEs) in China to study the application of blockchain technology in developing the real economy. Deep learning neural network is applied to the vulnerability analysis a...

Explainable Deep Learning Improves Physician Interpretation of Myocardial Perfusion Imaging.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Artificial intelligence may improve accuracy of myocardial perfusion imaging (MPI) but will likely be implemented as an aid to physician interpretation rather than an autonomous tool. Deep learning (DL) has high standalone diagnostic accuracy for obs...

A deep learning framework for enhancer prediction using word embedding and sequence generation.

Biophysical chemistry
Enhancers are non-coding DAN fragments that play key roles in gene regulations and can promote the transcription of structural genes, thereby affecting the expression of structural protein catalytic enzymes and regulatory proteins. Accurate identific...

Exploiting exercise electrocardiography to improve early diagnosis of atrial fibrillation with deep learning neural networks.

Computers in biology and medicine
Atrial fibrillation (AF) is the most common type of sustained arrhythmia. It results from abnormal irregularities in the electrical performance of the atria, and may cause heart thrombosis, stroke, arterial disease, thromboembolism, and heart failure...

Recent advancement in cancer diagnosis using machine learning and deep learning techniques: A comprehensive review.

Computers in biology and medicine
Being a second most cause of mortality worldwide, cancer has been identified as a perilous disease for human beings, where advance stage diagnosis may not help much in safeguarding patients from mortality. Thus, efforts to provide a sustainable archi...