AI Medical Compendium Topic

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X-Rays

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Multi-input adaptive neural network for automatic detection of cervical vertebral landmarks on X-rays.

Computers in biology and medicine
Cervical vertebral landmark detection is a significant pre-task for vertebral relative motion parameter measurement, which is helpful for doctors to diagnose cervical spine diseases. Accurate cervical vertebral landmark detection could provide reliab...

Tooth detection for each tooth type by application of faster R-CNNs to divided analysis areas of dental panoramic X-ray images.

Radiological physics and technology
This study aimed to propose a computerized method for detecting the tooth region for each tooth type as the initial stage in the development of a computer-aided diagnosis (CAD) scheme for dental panoramic X-ray images. Our database consists of 160 pa...

MHA-CoroCapsule: Multi-Head Attention Routing-Based Capsule Network for COVID-19 Chest X-Ray Image Classification.

IEEE transactions on medical imaging
The outbreak of COVID-19 threatens the lives and property safety of countless people and brings a tremendous pressure to health care systems worldwide. The principal challenge in the fight against this disease is the lack of efficient detection metho...

Using deep transfer learning to detect scoliosis and spondylolisthesis from x-ray images.

PloS one
Recent years have witnessed wider prevalence of vertebral column pathologies due to lifestyle changes, sedentary behaviors, or injuries. Spondylolisthesis and scoliosis are two of the most common ailments with an incidence of 5% and 3% in the United ...

Detection of Dental Diseases through X-Ray Images Using Neural Search Architecture Network.

Computational intelligence and neuroscience
An important aspect of the diagnosis procedure in daily clinical practice is the analysis of dental radiographs. This is because the dentist must interpret different types of problems related to teeth, including the tooth numbers and related diseases...

Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening?

BMC medical research methodology
BACKGROUND: The health crisis resulting from the global COVID-19 pandemic highlighted more than ever the need for rapid, reliable and safe methods of diagnosis and monitoring of respiratory diseases. To study pulmonary involvement in detail, one of t...

Study on transfer learning capabilities for pneumonia classification in chest-x-rays images.

Computer methods and programs in biomedicine
BACKGROUND: over the last year, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and its variants have highlighted the importance of screening tools with high diagnostic accuracy for new illnesses such as COVID-19. In that regard, dee...

Segmentation Performance Comparison Considering Regional Characteristics in Chest X-ray Using Deep Learning.

Sensors (Basel, Switzerland)
Chest radiography is one of the most widely used diagnostic methods in hospitals, but it is difficult to read clearly because several human organ tissues and bones overlap. Therefore, various image processing and rib segmentation methods have been pr...

CADxReport: Chest x-ray report generation using co-attention mechanism and reinforcement learning.

Computers in biology and medicine
BACKGROUND: Automated generation of radiological reports for different imaging modalities is essentially required to smoothen the clinical workflow and alleviate radiologists' workload. It involves the careful amalgamation of image processing techniq...

XctNet: Reconstruction network of volumetric images from a single X-ray image.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Conventional Computed Tomography (CT) produces volumetric images by computing inverse Radon transformation using X-ray projections from different angles, which results in high dose radiation, long reconstruction time and artifacts. Biologically, prio...