AIMC Topic: Deep Learning

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ECG Classification for Detecting ECG Arrhythmia Empowered with Deep Learning Approaches.

Computational intelligence and neuroscience
According to the World Health Organization (WHO) report, heart disease is spreading throughout the world very rapidly and the situation is becoming alarming in people aged 40 or above (Xu, 2020). Different methods and procedures are adopted to detect...

Analysis of the Relevance Environment between Marxist Philosophy and System Theory Based on Deep Learning.

Journal of environmental and public health
In social science and natural science, MP (Marxist Philosophy) has played an active role in promoting its development, and MP also guides people's practice and understanding. There is an inevitable connection with system theory MP. In a sense, both s...

A New Approach to Quantify and Grade Radiation Dermatitis Using Deep-Learning Segmentation in Skin Photographs.

Clinical oncology (Royal College of Radiologists (Great Britain))
AIMS: Objective evaluation of radiation dermatitis is important for analysing the correlation between the severity of radiation dermatitis and dose distribution in clinical practice and for reliable reporting in clinical trials. We developed a novel ...

Comparative Study of Raw Ultrasound Data Representations in Deep Learning to Classify Hepatic Steatosis.

Ultrasound in medicine & biology
Adiposity accumulation in the liver is an early-stage indicator of non-alcoholic fatty liver disease. Analysis of ultrasound (US) backscatter echoes from liver parenchyma with deep learning (DL) may offer an affordable alternative for hepatic steatos...

Deep learning in ultrasound elastography imaging: A review.

Medical physics
It is known that changes in the mechanical properties of tissues are associated with the onset and progression of certain diseases. Ultrasound elastography is a technique to characterize tissue stiffness using ultrasound imaging either by measuring t...

Sea Cucumber Detection Algorithm Based on Deep Learning.

Sensors (Basel, Switzerland)
The traditional single-shot multiBox detector (SSD) for the recognition process in sea cucumbers has problems, such as an insufficient expression of features, heavy computation, and difficulty in application to embedded platforms. To solve these prob...

Modelling monthly pan evaporation utilising Random Forest and deep learning algorithms.

Scientific reports
Evaporation is the primary aspect causing water loss in the hydrological cycle; therefore, water loss must be precisely measured. Evaporation is an intricate nonlinear process occurring as a result of several climatic aspects. The purpose of this res...

Detecting COVID-19 patients via MLES-Net deep learning models from X-Ray images.

BMC medical imaging
BACKGROUND: Corona Virus Disease 2019 (COVID-19) first appeared in December 2019, and spread rapidly around the world. COVID-19 is a pneumonia caused by novel coronavirus infection in 2019. COVID-19 is highly infectious and transmissible. By 7 May 20...

An Effective Approach of Vehicle Detection Using Deep Learning.

Computational intelligence and neuroscience
With the rise of unmanned driving and intelligent transportation research, great progress has been made in vehicle detection technology. The purpose of this paper is employing the method of deep learning to study the vehicle detection algorithm, in w...

Detection of Pneumonia Infection by Using Deep Learning on a Mobile Platform.

Computational intelligence and neuroscience
Pneumonia is a disease that spreads quickly and poses a serious risk to the health and well-being of its victims. An accurate biomedical diagnosis of pneumonia necessitates the use of various diagnostic tools and the evaluation of various clinical fe...