AIMC Topic: Neural Networks, Computer

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Multi-Class Skin Problem Classification Using Deep Generative Adversarial Network (DGAN).

Computational intelligence and neuroscience
The lack of annotated datasets makes the automatic detection of skin problems very difficult, which is also the case for most other medical applications. The outstanding results achieved by deep learning techniques in developing such applications hav...

Investigation of Effectiveness of Shuffled Frog-Leaping Optimizer in Training a Convolution Neural Network.

Journal of healthcare engineering
One of the leading algorithms and architectures in deep learning is Convolution Neural Network (CNN). It represents a unique method for image processing, object detection, and classification. CNN has shown to be an efficient approach in the machine l...

Research on Lung Ultrasound Image Classification Based on Compressed Sensing.

Journal of healthcare engineering
Pneumothorax is a common injury in disaster rescue, traffic accidents, and war trauma environments and requires early diagnosis and treatment. The commonly used X-ray, CT, and other diagnostic instruments are not suitable for rescue sites due to thei...

Deep convolutional neural network-based signal quality assessment for photoplethysmogram.

Computers in biology and medicine
Quality assessment of bio-signals is important to prevent clinical misdiagnosis. With the introduction of mobile and wearable health care, it is becoming increasingly important to distinguish available signals from noise. The goal of this study was t...

Many-Body Neural Network-Based Force Field for Structure-Based Coarse-Graining of Water.

The journal of physical chemistry. A
High-fidelity results from atomistic simulations can only be obtained by using accurate force-field (FF) parameters. Although empirical FFs are commonly used in the modeling of atomistic systems due to their simplicity, they have many limitations inh...

Data-driven discovery of Green's functions with human-understandable deep learning.

Scientific reports
There is an opportunity for deep learning to revolutionize science and technology by revealing its findings in a human interpretable manner. To do this, we develop a novel data-driven approach for creating a human-machine partnership to accelerate sc...

Weakly supervised end-to-end artificial intelligence in gastrointestinal endoscopy.

Scientific reports
Artificial intelligence (AI) is widely used to analyze gastrointestinal (GI) endoscopy image data. AI has led to several clinically approved algorithms for polyp detection, but application of AI beyond this specific task is limited by the high cost o...

Ultrafast neuromorphic photonic image processing with a VCSEL neuron.

Scientific reports
The ever-increasing demand for artificial intelligence (AI) systems is underlining a significant requirement for new, AI-optimised hardware. Neuromorphic (brain-like) processors are one highly-promising solution, with photonic-enabled realizations re...

A deep-learning approach for online cell identification and trace extraction in functional two-photon calcium imaging.

Nature communications
In vivo two-photon calcium imaging is a powerful approach in neuroscience. However, processing two-photon calcium imaging data is computationally intensive and time-consuming, making online frame-by-frame analysis challenging. This is especially true...

Development of CNN models for the enteral feeding tube positioning assessment on a small scale data set.

BMC medical imaging
BACKGROUND: Enteral nutrition through feeding tubes serves as the primary method of nutritional supplementation for patients unable to feed themselves. Plain radiographs are routinely used to confirm the position of the Nasoenteric feeding tubes the ...