AI Medical Compendium Journal:
Journal of X-ray science and technology

Showing 11 to 20 of 116 articles

MAFA-Uformer: Multi-attention and dual-branch feature aggregation U-shaped transformer for sparse-view CT reconstruction.

Journal of X-ray science and technology Jan 8, 2025
BACKGROUND: Although computed tomography (CT) is widely employed in disease detection, X-ray radiation may pose a risk to the health of patients. Reducing the projection views is a common method, however, the reconstructed images often suffer from st...
Image Processing, Computer-Assisted Humans Artifacts Neural Networks, Computer Phantoms, Imaging Algorithms Tomography, X-Ray Computed
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Enhanced swin transformer based tuberculosis classification with segmentation using chest X-ray.

Journal of X-ray science and technology Jan 8, 2025
BACKGROUND:: Tuberculosis disease is the disease that causes significant morbidity and mortality worldwide. Thus, early detection of the disease is crucial for proper treatment and controlling the spread of Tuberculosis disease. Chest X-ray imaging i...
Humans Neural Networks, Computer Algorithms Tuberculosis Tuberculosis, Pulmonary Deep Learning Radiography, Thoracic Image Processing, Computer-Assisted Radiographic Image Interpretation, Computer-Assisted
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Mask R-CNN assisted diagnosis of spinal tuberculosis.

Journal of X-ray science and technology Dec 24, 2024
The prevalence of spinal tuberculosis (ST) is particularly high in underdeveloped regions with inadequate medical conditions. This not only leads to misdiagnosis and delays in treatment progress but also contributes to the continued transmission of t...
Deep Learning Tuberculosis, Spinal Neural Networks, Computer Humans Tomography, X-Ray Computed Diagnosis, Computer-Assisted
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Spine X-ray image segmentation based on deep learning and marker controlled watershed.

Journal of X-ray science and technology Dec 19, 2024
BACKGROUND: The development of automatic methods for vertebral segmentation provides the objective analysis of each vertebra in the spine image, which is important for the diagnosis of various spinal diseases. However, vertebrae have inter-class simi...
Algorithms Deep Learning Image Processing, Computer-Assisted Humans Radiography Spine
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Quantitative analysis of deep learning reconstruction in CT angiography: Enhancing CNR and reducing dose.

Journal of X-ray science and technology Dec 18, 2024
BACKGROUND: Computed tomography angiography (CTA) provides significant information on image quality in vascular imaging, thus offering high-resolution images despite having the disadvantages of increased radiation doses and contrast agent-related sid...
Computed Tomography Angiography Radiation Dosage Signal-To-Noise Ratio Phantoms, Imaging Contrast Media Radiographic Image Interpretation, Computer-Assisted Humans Deep Learning Image Processing, Computer-Assisted
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Radiomics and deep learning features of pericoronary adipose tissue on non-contrast computerized tomography for predicting non-calcified plaques.

Journal of X-ray science and technology Dec 18, 2024
BACKGROUND: Inflammation of coronary arterial plaque is considered a key factor in the development of coronary heart disease. Early the plaque detection and timely treatment of the atherosclerosis could effectively reduce the risk of cardiovascular e...
Adipose Tissue Coronary Artery Disease Coronary Vessels Male Middle Aged Deep Learning Plaque, Atherosclerotic Epicardial Adipose Tissue Humans Tomography, X-Ray Computed Female Aged Radiomics
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CT image super-resolution under the guidance of deep gradient information.

Journal of X-ray science and technology Dec 15, 2024
Due to the hardware constraints of Computed Tomography (CT) imaging, acquiring high-resolution (HR) CT images in clinical settings poses a significant challenge. In recent years, convolutional neural networks have shown great potential in CT super-re...
Deep Learning Image Processing, Computer-Assisted Phantoms, Imaging Neural Networks, Computer Algorithms Tomography, X-Ray Computed Humans
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Enhancing brain tumor classification by integrating radiomics and deep learning features: A comprehensive study utilizing ensemble methods on MRI scans.

Journal of X-ray science and technology Dec 9, 2024
BACKGROUND AND OBJECTIVE: This study aims to assess the effectiveness of combining radiomics features (RFs) with deep learning features (DFs) for classifying brain tumors-specifically Glioma, Meningioma, and Pituitary Tumor-using MRI scans and advanc...
Brain Neoplasms Male Middle Aged Deep Learning Image Processing, Computer-Assisted Glioma Female Adult Radiomics Humans Neural Networks, Computer Brain Support Vector Machine Magnetic Resonance Imaging
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Bonevoyage: Navigating the depths of osteoporosis detection with a dual-core ensemble of cascaded ShuffleNet and neural networks.

Journal of X-ray science and technology Nov 27, 2024
BACKGROUND: Osteoporosis (OP) is a condition that significantly decreases bone density and strength, often remaining undetected until the occurrence of a fracture. Timely identification of OP is essential for preventing fractures, reducing morbidity,...
Algorithms Humans Bone Density Neural Networks, Computer Osteoporosis Absorptiometry, Photon Female Reproducibility of Results Sensitivity and Specificity
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MRI classification and discrimination of spinal schwannoma and meningioma based on deep learning.

Journal of X-ray science and technology Nov 27, 2024
BACKGROUD: Schwannoma (SCH) and meningiomas (MEN) are the two most common primary spinal cord tumors. Differentiating between them preoperatively remains a clinical challenge due to the substantial overlap in their clinical presentation and imaging c...
Male Meningioma Middle Aged Magnetic Resonance Imaging Humans Meningeal Neoplasms Female Diagnosis, Differential Deep Learning Spinal Cord Neoplasms Retrospective Studies Aged Neurilemmoma Adult
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