AIMC Topic: Deep Learning

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PlncRNA-HDeep: plant long noncoding RNA prediction using hybrid deep learning based on two encoding styles.

BMC bioinformatics
BACKGROUND: Long noncoding RNAs (lncRNAs) play an important role in regulating biological activities and their prediction is significant for exploring biological processes. Long short-term memory (LSTM) and convolutional neural network (CNN) can auto...

Deep learning image reconstruction for pancreatic low-dose computed tomography: comparison with hybrid iterative reconstruction.

Abdominal radiology (New York)
PURPOSE: To evaluate image quality, image noise, and conspicuity of pancreatic ductal adenocarcinoma (PDAC) in pancreatic low-dose computed tomography (LDCT) reconstructed using deep learning image reconstruction (DLIR) and compare with those of imag...

Automated detection of acute respiratory distress syndrome from chest X-Rays using Directionality Measure and deep learning features.

Computers in biology and medicine
Acute respiratory distress syndrome (ARDS) is a life-threatening lung injury with global prevalence and high mortality. Chest x-rays (CXR) are critical in the early diagnosis and treatment of ARDS. However, imaging findings may not result in proper i...

A comparative study of deep learning-based network model and conventional method to assess beach debris standing-stock.

Marine pollution bulletin
The conventional survey of marine debris standing-stock has various drawbacks such as high cost and inaccuracy because the total amount of debris in the whole beach is inferred using the results of the manual investigation in selected narrow areas. T...

Rice leaf diseases prediction using deep neural networks with transfer learning.

Environmental research
Rice (Oryza sativa) is a principal cereal crop in the world. It is consumed by greater than half of the world's population as a staple food for energy source. The yield production quantity and quality of the rice grain is affecting by abiotic and bio...

Deep Learning Model for Automated Detection and Classification of Central Canal, Lateral Recess, and Neural Foraminal Stenosis at Lumbar Spine MRI.

Radiology
Background Assessment of lumbar spinal stenosis at MRI is repetitive and time consuming. Deep learning (DL) could improve -productivity and the consistency of reporting. Purpose To develop a DL model for automated detection and classification of lumb...

A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data.

Sensors (Basel, Switzerland)
Sleep disturbances are common in Alzheimer's disease and other neurodegenerative disorders, and together represent a potential therapeutic target for disease modification. A major barrier for studying sleep in patients with dementia is the requiremen...

Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-Ray Images.

IEEE journal of biomedical and health informatics
Coronavirus disease 2019 (COVID-19) is one of the most destructive pandemic after millennium, forcing the world to tackle a health crisis. Automated lung infections classification using chest X-ray (CXR) images could strengthen diagnostic capability ...