AIMC Topic: Deep Learning

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Development of computer-aided model to differentiate COVID-19 from pulmonary edema in lung CT scan: EDECOVID-net.

Computers in biology and medicine
The efforts made to prevent the spread of COVID-19 face specific challenges in diagnosing COVID-19 patients and differentiating them from patients with pulmonary edema. Although systemically administered pulmonary vasodilators and acetazolamide are o...

Deep Learning Approach at the Edge to Detect Iron Ore Type.

Sensors (Basel, Switzerland)
There is a constant risk of iron ore collapsing during its transfer between processing stages in beneficiation plants. Existing instrumentation is not only expensive but also complex and challenging to maintain. In this research, we propose using edg...

Deep learning approaches for data-independent acquisition proteomics.

Expert review of proteomics
INTRODUCTION: Data-independent acquisition (DIA) is an emerging technology for large-scale proteomic studies. DIA data analysis methods are evolving rapidly, and deep learning has cut a conspicuous figure in this field.

Data Homogeneity Effect in Deep Learning-Based Prediction of Type 1 Diabetic Retinopathy.

Journal of diabetes research
This study is aimed at evaluating a deep transfer learning-based model for identifying diabetic retinopathy (DR) that was trained using a dataset with high variability and predominant type 2 diabetes (T2D) and comparing model performance with that in...

Software Defect Prediction Based on Hybrid Swarm Intelligence and Deep Learning.

Computational intelligence and neuroscience
In order to improve software quality and testing efficiency, this paper implements the prediction of software defects based on deep learning. According to the respective advantages and disadvantages of the particle swarm algorithm and the wolf swarm ...

Rigid motion-resolved prediction using deep learning for real-time parallel-transmission pulse design.

Magnetic resonance in medicine
PURPOSE: Tailored parallel-transmit (pTx) pulses produce uniform excitation profiles at 7 T, but are sensitive to head motion. A potential solution is real-time pulse redesign. A deep learning framework is proposed to estimate pTx distributions foll...

Deep learning radiomics model related with genomics phenotypes for lymph node metastasis prediction in colorectal cancer.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: The preoperative lymph node (LN) status is important for the treatment of colorectal cancer (CRC). Here, we established and validated a deep learning (DPL) model for predicting lymph node metastasis (LNM) in CRC.

A Novel Deep Learning-Based System for Triage in the Emergency Department Using Electronic Medical Records: Retrospective Cohort Study.

Journal of medical Internet research
BACKGROUND: Emergency department (ED) crowding has resulted in delayed patient treatment and has become a universal health care problem. Although a triage system, such as the 5-level emergency severity index, somewhat improves the process of ED treat...

Ensemble of Deep Learning Based Clinical Decision Support System for Chronic Kidney Disease Diagnosis in Medical Internet of Things Environment.

Computational intelligence and neuroscience
Recently, Internet of Things (IoT) and cloud computing environments become commonly employed in several healthcare applications by the integration of monitoring things such as sensors and medical gadgets for observing remote patients. For availing of...

Robust Real-Time Traffic Surveillance with Deep Learning.

Computational intelligence and neuroscience
Real-time vehicle monitoring in highways, roads, and streets may provide useful data both for infrastructure planning and for traffic management in general. Even though it is a classic research area in computer vision, advances in neural networks for...