AIMC Topic: Neural Networks, Computer

Clear Filters Showing 17071 to 17080 of 31376 articles

Video recognition of simple mastoidectomy using convolutional neural networks: Detection and segmentation of surgical tools and anatomical regions.

Computer methods and programs in biomedicine
A simple mastoidectomy is used to remove inflammation of the mastoid cavity and to create a route to the skull base and middle ear. However, due to the complexity and difficulty of the simple mastoidectomy, implementing robot vision for assisted surg...

Automatic Detection and Classification of Multiple Catheters in Neonatal Radiographs with Deep Learning.

Journal of digital imaging
We develop and evaluate a deep learning algorithm to classify multiple catheters on neonatal chest and abdominal radiographs. A convolutional neural network (CNN) was trained using a dataset of 777 neonatal chest and abdominal radiographs, with a spl...

Classification of Space Objects by Using Deep Learning with Micro-Doppler Signature Images.

Sensors (Basel, Switzerland)
Radar target classification is an important task in the missile defense system. State-of-the-art studies using micro-doppler frequency have been conducted to classify the space object targets. However, existing studies rely highly on feature extracti...

Confidence Score: The Forgotten Dimension of Object Detection Performance Evaluation.

Sensors (Basel, Switzerland)
When deploying a model for object detection, a confidence score threshold is chosen to filter out false positives and ensure that a predicted bounding box has a certain minimum score. To achieve state-of-the-art performance on benchmark datasets, mos...

Portable Interactive Pulse Tactile Recorder and Player System.

Sensors (Basel, Switzerland)
Pulse palpation is an effective method for diagnosing arterial diseases. However, most pulse measurement devices use preconfigured pressures to collect pulse signals, and most pulse tactile simulators can only display standard or predefined pulse wav...

A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG.

PloS one
Autism spectrum disorder (ASD) is a developmental disability characterized by persistent impairments in social interaction, speech and nonverbal communication, and restricted or repetitive behaviors. Currently Electroencephalography (EEG) is the most...

Quality control stress test for deep learning-based diagnostic model in digital pathology.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Digital pathology provides a possibility for computational analysis of histological slides and automatization of routine pathological tasks. Histological slides are very heterogeneous concerning staining, sections' thickness, and artifacts arising du...

Object and anatomical feature recognition in surgical video images based on a convolutional neural network.

International journal of computer assisted radiology and surgery
PURPOSE: Artificial intelligence-enabled techniques can process large amounts of surgical data and may be utilized for clinical decision support to recognize or forecast adverse events in an actual intraoperative scenario. To develop an image-guided ...

Predictive modelling of piezometric head and seepage discharge in earth dam using soft computational models.

Environmental science and pollution research international
Predictions of pore pressure and seepage discharge are the most important parameters in the design of earth dams and assessing their safety during the operational period as well. In this research, soft computing models namely multi-layer perceptron n...

Classification of impedance cardiography dZ/dt complex subtypes using pattern recognition artificial neural networks.

Biomedizinische Technik. Biomedical engineering
In impedance cardiography (ICG), the detection of dZ/dt signal (ICG) characteristic points, especially the X point, is a crucial step for the calculation of hemodynamic parameters such as stroke volume (SV) and cardiac output (CO). Unfortunately, for...