AIMC Topic: Image Processing, Computer-Assisted

Clear Filters Showing 2751 to 2760 of 10288 articles

Deep Learning Based Cystoscopy Image Enhancement.

Journal of endourology
Endoscopy image enhancement technology provides doctors with clearer and more detailed images for observation and diagnosis, allowing doctors to assess lesions more accurately. Unlike most other endoscopy images, cystoscopy images face more complex ...

On the evaluation of deep learning interpretability methods for medical images under the scope of faithfulness.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Evaluating the interpretability of Deep Learning models is crucial for building trust and gaining insights into their decision-making processes. In this work, we employ class activation map based attribution methods in a set...

Arterial Distension Monitoring Scheme Using FPGA-Based Inference Machine in Ultrasound Scanner Circuit System.

IEEE transactions on biomedical circuits and systems
This paper presents an arterial distension monitoring scheme using a field-programmable gate array (FPGA)-based inference machine in an ultrasound scanner circuit system. An arterial distension monitoring requires a precise positioning of an ultrasou...

ECG-Image-Kit: a synthetic image generation toolbox to facilitate deep learning-based electrocardiogram digitization.

Physiological measurement
Cardiovascular diseases are a major cause of mortality globally, and electrocardiograms (ECGs) are crucial for diagnosing them. Traditionally, ECGs are stored in printed formats. However, these printouts, even when scanned, are incompatible with adva...

Deep-learning-based pelvic automatic segmentation in pelvic fractures.

Scientific reports
With the recent increase in traffic accidents, pelvic fractures are increasing, second only to skull fractures, in terms of mortality and risk of complications. Research is actively being conducted on the treatment of intra-abdominal bleeding, the pr...

Impact of functional electrical stimulation on nerve-damaged muscles by quantifying fat infiltration using deep learning.

Scientific reports
Quantitative imaging in life sciences has evolved into a powerful approach combining advanced microscopy acquisition and automated analysis of image data. The focus of the present study is on the imaging-based evaluation of the posterior cricoaryteno...

Deep learning for identifying bee species from images of wings and pinned specimens.

PloS one
One of the most challenging aspects of bee ecology and conservation is species-level identification, which is costly, time consuming, and requires taxonomic expertise. Recent advances in the application of deep learning and computer vision have shown...

DTDO: Driving Training Development Optimization enabled deep learning approach for brain tumour classification using MRI.

Network (Bristol, England)
A brain tumour is an abnormal mass of tissue. Brain tumours vary in size, from tiny to large. Moreover, they display variations in location, shape, and size, which add complexity to their detection. The accurate delineation of tumour regions poses a ...

Optimizing double-layered convolutional neural networks for efficient lung cancer classification through hyperparameter optimization and advanced image pre-processing techniques.

BMC medical informatics and decision making
Lung cancer remains a leading cause of cancer-related mortality globally, with prognosis significantly dependent on early-stage detection. Traditional diagnostic methods, though effective, often face challenges regarding accuracy, early detection, an...

Cervical Spondylosis Diagnosis Based on Convolutional Neural Network with X-ray Images.

Sensors (Basel, Switzerland)
The increase in Cervical Spondylosis cases and the expansion of the affected demographic to younger patients have escalated the demand for X-ray screening. Challenges include variability in imaging technology, differences in equipment specifications,...