AIMC Topic: Deep Learning

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A deep learning radiomics analysis for identifying sinus invasion in patients with meningioma before operation using tumor and peritumoral regions.

European journal of radiology
BACKGROUND: For patients with meningioma, surgical procedures are different because of the status of sinus invasion. However, there is still no suitable technique to identify the status of sinus invasion in patients with meningiomas. We aimed to buil...

Internet of Things with Deep Learning-Based Face Recognition Approach for Authentication in Control Medical Systems.

Computational and mathematical methods in medicine
Internet of Things (IoT) with deep learning (DL) is drastically growing and plays a significant role in many applications, including medical and healthcare systems. It can help users in this field get an advantage in terms of enhanced touchless authe...

Experimental Research on the Antitumor Effect of Human Gastric Cancer Cells Transplanted in Nude Mice Based on Deep Learning Combined with Spleen-Invigorating Chinese Medicine.

Computational and mathematical methods in medicine
Gastric cancer is still the fifth most common malignant tumor in the world and has the fourth highest mortality rate in the world. Gastric cancer is difficult to treat because of its unobvious onset, low resection rate, and rapid deterioration. There...

The Construction of Online Course Learning Model of Piano Education from the Perspective of Deep Learning.

Computational intelligence and neuroscience
This exploration aims at solving multiple teaching problems in piano online education course. On the premise of collaborative filtering, the K-means clustering algorithm is employed to apply the time data to the neural collaborative filtering algorit...

Application of Deep Learning to Ischemic and Hemorrhagic Stroke Computed Tomography and Magnetic Resonance Imaging.

Seminars in ultrasound, CT, and MR
Deep Learning (DL) algorithm holds great potential in the field of stroke imaging. It has been applied not only to the "downstream" side such as lesion detection, treatment decision making, and outcome prediction, but also to the "upstream" side for ...

Contextual Detection of Pedestrians and Vehicles in Orthophotography by Fusion of Deep Learning Algorithms.

Sensors (Basel, Switzerland)
In the context of smart cities, monitoring pedestrian and vehicle movements is essential to recognize abnormal events and prevent accidents. The proposed method in this work focuses on analyzing video streams captured from a vertically installed came...

An Efficient Deep Learning Model to Detect COVID-19 Using Chest X-ray Images.

International journal of environmental research and public health
The tragic pandemic of COVID-19, due to the Severe Acute Respiratory Syndrome coronavirus-2 or SARS-CoV-2, has shaken the entire world, and has significantly disrupted healthcare systems in many countries. Because of the existing challenges and contr...

Deep learning-based high-accuracy quantitation for lumbar intervertebral disc degeneration from MRI.

Nature communications
To help doctors and patients evaluate lumbar intervertebral disc degeneration (IVDD) accurately and efficiently, we propose a segmentation network and a quantitation method for IVDD from T2MRI. A semantic segmentation network (BianqueNet) composed of...

Deep learning for gastroscopic images: computer-aided techniques for clinicians.

Biomedical engineering online
Gastric disease is a major health problem worldwide. Gastroscopy is the main method and the gold standard used to screen and diagnose many gastric diseases. However, several factors, such as the experience and fatigue of endoscopists, limit its perfo...

Examination of blood samples using deep learning and mobile microscopy.

BMC bioinformatics
BACKGROUND: Microscopic examination of human blood samples is an excellent opportunity to assess general health status and diagnose diseases. Conventional blood tests are performed in medical laboratories by specialized professionals and are time and...