AIMC Topic: Deep Learning

Clear Filters Showing 16291 to 16300 of 28423 articles

Predicting the evolution of number of native contacts of a small protein by using deep learning approach.

Computational biology and chemistry
Native contacts (NCs) are one of the most vital parameters in order to define the resemblance of a protein conformation with its native state. Prediction of number of native contacts in a protein is useful in protein folding mechanism. In this work, ...

Automatic coronavirus disease 2019 diagnosis based on chest radiography and deep learning - Success story or dataset bias?

Medical physics
PURPOSE: Over the last 2 years, the artificial intelligence (AI) community has presented several automatic screening tools for coronavirus disease 2019 (COVID-19) based on chest radiography (CXR), with reported accuracies often well over 90%. However...

Bangla Sign Language (BdSL) Alphabets and Numerals Classification Using a Deep Learning Model.

Sensors (Basel, Switzerland)
A real-time Bangla Sign Language interpreter can enable more than 200 k hearing and speech-impaired people to the mainstream workforce in Bangladesh. Bangla Sign Language (BdSL) recognition and detection is a challenging topic in computer vision and ...

Self-relabeling for noise-tolerant retina vessel segmentation through label reliability estimation.

BMC medical imaging
BACKGROUND: Retinal vessel segmentation benefits significantly from deep learning. Its performance relies on sufficient training images with accurate ground-truth segmentation, which are usually manually annotated in the form of binary pixel-wise lab...

Design of Optimal Deep Learning-Based Pancreatic Tumor and Nontumor Classification Model Using Computed Tomography Scans.

Journal of healthcare engineering
Pancreatic tumor is a lethal kind of tumor and its prediction is really poor in the current scenario. Automated pancreatic tumor classification using computer-aided diagnosis (CAD) model is necessary to track, predict, and classify the existence of p...

Computational Intelligence Method for Detection of White Blood Cells Using Hybrid of Convolutional Deep Learning and SIFT.

Computational and mathematical methods in medicine
Infection diseases are among the top global issues with negative impacts on health, economy, and society as a whole. One of the most effective ways to detect these diseases is done by analysing the microscopic images of blood cells. Artificial intell...

Deep Learning Model for the Automatic Classification of White Blood Cells.

Computational intelligence and neuroscience
Blood cell count is highly useful in identifying the occurrence of a particular disease or ailment. To successfully measure the blood cell count, sophisticated equipment that makes use of invasive methods to acquire the blood cell slides or images is...

A Hybrid Framework for Intrusion Detection in Healthcare Systems Using Deep Learning.

Frontiers in public health
The unbounded increase in network traffic and user data has made it difficult for network intrusion detection systems to be abreast and perform well. Intrusion Systems are crucial in e-healthcare since the patients' medical records should be kept hig...

A simultaneous multi-slice T mapping framework based on overlapping-echo detachment planar imaging and deep learning reconstruction.

Magnetic resonance in medicine
PURPOSE: Quantitative MRI (qMRI) is of great importance to clinical medicine and scientific research. However, most qMRI techniques are time-consuming and sensitive to motion, especially when a large 3D volume is imaged. To accelerate the acquisition...

Uncharted Waters of Machine and Deep Learning for Surgical Phase Recognition in Neurosurgery.

World neurosurgery
Recent years have witnessed artificial intelligence (AI) make meteoric leaps in both medicine and surgery, bridging the gap between the capabilities of humans and machines. Digitization of operating rooms and the creation of massive quantities of dat...