AIMC Topic: X-Rays

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Multiclass Classification of Chest X-Ray Images for the Prediction of COVID-19 Using Capsule Network.

Computational intelligence and neuroscience
It is critical to establish a reliable method for detecting people infected with COVID-19 since the pandemic has numerous harmful consequences worldwide. If the patient is infected with COVID-19, a chest X-ray can be used to determine this. In this w...

Diagnostic accuracy of a commercially available, deep learning-based chest X-ray interpretation software for detecting culture-confirmed pulmonary tuberculosis.

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases
BACKGROUND: Few evaluations of computer-aided detection (CAD) software for analyzing chest radiographs for tuberculosis have used mycobacterial culture as the reference standard.

End-to-end deep learning for interior tomography with low-dose x-ray CT.

Physics in medicine and biology
There are several x-ray computed tomography (CT) scanning strategies used to reduce radiation dose, such as (1) sparse-view CT, (2) low-dose CT and (3) region-of-interest (ROI) CT (called interior tomography). To further reduce the dose, sparse-view ...

A lightweight CNN-based network on COVID-19 detection using X-ray and CT images.

Computers in biology and medicine
BACKGROUND AND OBJECTIVES: The traditional method of detecting COVID-19 disease mainly rely on the interpretation of computer tomography (CT) or X-ray images (X-ray) by doctors or professional researchers to identify whether it is COVID-19 disease, w...

Early severity prediction of BPD for premature infants from chest X-ray images using deep learning: A study at the 28th day of oxygen inhalation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Bronchopulmonary dysplasia is a common respiratory disease in premature infants. The severity is diagnosed at the 56th day after birth or discharge by analyzing the clinical indicators, which may cause the delay of the best ...

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...