AIMC Topic: Deep Learning

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A Review on Multiscale-Deep-Learning Applications.

Sensors (Basel, Switzerland)
In general, most of the existing convolutional neural network (CNN)-based deep-learning models suffer from spatial-information loss and inadequate feature-representation issues. This is due to their inability to capture multiscale-context information...

Improved Monitoring of Wildlife Invasion through Data Augmentation by Extract-Append of a Segmented Entity.

Sensors (Basel, Switzerland)
Owing to the continuous increase in the damage to farms due to wild animals' destruction of crops in South Korea, various methods have been proposed to resolve these issues, such as installing electric fences and using warning lamps or ultrasonic wav...

Deep Learning Models for Classification of Dental Diseases Using Orthopantomography X-ray OPG Images.

Sensors (Basel, Switzerland)
The teeth are the most challenging material to work with in the human body. Existing methods for detecting teeth problems are characterised by low efficiency, the complexity of the experiential operation, and a higher level of user intervention. Olde...

A novel hybrid framework for metabolic pathways prediction based on the graph attention network.

BMC bioinformatics
BACKGROUND: Making clear what kinds of metabolic pathways a drug compound involves in can help researchers understand how the drug is absorbed, distributed, metabolized, and excreted. The characteristics of a compound such as structure, composition a...

Deep learning alignment of bidirectional raster scanning in high speed photoacoustic microscopy.

Scientific reports
Simultaneous point-by-point raster scanning of optical and acoustic beams has been widely adapted to high-speed photoacoustic microscopy (PAM) using a water-immersible microelectromechanical system or galvanometer scanner. However, when using high-sp...

Improving breast cancer diagnostics with deep learning for MRI.

Science translational medicine
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has a high sensitivity in detecting breast cancer but often leads to unnecessary biopsies and patient workup. We used a deep learning (DL) system to improve the overall accuracy of breast...

Multiple Statistical Model Ensemble Predictions of Residual Chlorine in Drinking Water: Applications of Various Deep Learning and Machine Learning Algorithms.

Journal of environmental and public health
Applicability of statistical models in predicting chlorine decay remains minimally explored. This study predicted residual chlorine using six deep learning and nine machine learning techniques. Suitability of multimodel ensembles (MMEs) including ari...

Early Diagnosis of Tuberculosis Using Deep Learning Approach for IOT Based Healthcare Applications.

Computational intelligence and neuroscience
In the modern world, Tuberculosis (TB) is regarded as a serious health issue with a high rate of mortality. TB can be cured completely by early diagnosis. For achieving this, one tool utilized is CXR (Chest X-rays) which is used to screen active TB. ...

Deep Learning-Based Classification of Spoken English Digits.

Computational intelligence and neuroscience
Classification of isolated digits is the basic challenge for many speech classification systems. While a lot of work has been carried out on spoken languages, only limited research work on spoken English digit data has been reported in the literature...

A Novel Feature Selection with Hybrid Deep Learning Based Heart Disease Detection and Classification in the e-Healthcare Environment.

Computational intelligence and neuroscience
With the advancements in data mining, wearables, and cloud computing, online disease diagnosis services have been widely employed in the e-healthcare environment and improved the quality of the services. The e-healthcare services help to reduce the d...