AIMC Topic: Deep Learning

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A New Optimal Diagnosis System for Coronavirus (COVID-19) Diagnosis Based on Archimedes Optimization Algorithm on Chest X-Ray Images.

Computational intelligence and neuroscience
The new coronavirus, COVID-19, has affected people all over the world. Coronaviruses are a large group of viruses that can infect animals and humans and cause respiratory distress; these discomforts may be as mild as a cold or as severe as pneumonia....

DeepHistoClass: A Novel Strategy for Confident Classification of Immunohistochemistry Images Using Deep Learning.

Molecular & cellular proteomics : MCP
A multitude of efforts worldwide aim to create a single-cell reference map of the human body, for fundamental understanding of human health, molecular medicine, and targeted treatment. Antibody-based proteomics using immunohistochemistry (IHC) has pr...

Layered deep learning for automatic mandibular segmentation in cone-beam computed tomography.

Journal of dentistry
OBJECTIVE: To develop and validate a layered deep learning algorithm which automatically creates three-dimensional (3D) surface models of the human mandible out of cone-beam computed tomography (CBCT) imaging.

Deep Learning Algorithm Predicts Angiographic Coronary Artery Disease in Stable Patients Using Only a Standard 12-Lead Electrocardiogram.

The Canadian journal of cardiology
BACKGROUND: Current electrocardiogram analysis algorithms cannot predict the presence of coronary artery disease (CAD), especially in stable patients. This study assessed the ability of an artificial intelligence algorithm (ECGio; HEARTio Inc, Pittsb...

Classification of radioxenon spectra with deep learning algorithm.

Journal of environmental radioactivity
In this study, we propose for the first time a model of classification for Beta-Gamma coincidence radioxenon spectra using a deep learning approach through the convolution neural network (CNN) technique. We utilize the entire spectrum of actual data ...

Generalized Deep Learning EEG Models for Cross-Participant and Cross-Task Detection of the Vigilance Decrement in Sustained Attention Tasks.

Sensors (Basel, Switzerland)
Tasks which require sustained attention over a lengthy period of time have been a focal point of cognitive fatigue research for decades, with these tasks including air traffic control, watchkeeping, baggage inspection, and many others. Recent researc...

Effect of Patient Clinical Variables in Osteoporosis Classification Using Hip X-rays in Deep Learning Analysis.

Medicina (Kaunas, Lithuania)
: A few deep learning studies have reported that combining image features with patient variables enhanced identification accuracy compared with image-only models. However, previous studies have not statistically reported the additional effect of pati...

Predicting and characterizing a cancer dependency map of tumors with deep learning.

Science advances
Genome-wide loss-of-function screens have revealed genes essential for cancer cell proliferation, called cancer dependencies. It remains challenging to link cancer dependencies to the molecular compositions of cancer cells or to unscreened cell lines...

Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea.

The Science of the total environment
Prediction of rice yields at pixel scale rather than county scale can benefit crop management and scientific understanding because it is useful for monitoring how crop yields respond to various agricultural systems and environmental factors. In this ...

Protein inter-residue contact and distance prediction by coupling complementary coevolution features with deep residual networks in CASP14.

Proteins
This article reports and analyzes the results of protein contact and distance prediction by our methods in the 14th Critical Assessment of techniques for protein Structure Prediction (CASP14). A new deep learning-based contact/distance predictor was ...