AIMC Topic: Lung

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Multi-axis transformer based U-Net with class balanced ensemble model for lung disease classification using X-ray images.

Journal of X-ray science and technology Feb 26, 2025
Chest X-rays are an essential diagnostic tool for identifying chest disorders because of its high sensitivity in detecting pathological anomalies in the lungs. Classification models based on conventional Convolutional Neural Networks (CNNs) are adve...
Radiographic Image Interpretation, Computer-Assisted Lung Diseases Radiography, Thoracic Lung Deep Learning Image Processing, Computer-Assisted Humans Neural Networks, Computer Algorithms
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A hybrid inception-dilated-ResNet architecture for deep learning-based prediction of COVID-19 severity.

Scientific reports Feb 22, 2025
Chest computed tomography (CT) scans are essential for accurately assessing the severity of the novel Coronavirus (COVID-19), facilitating appropriate therapeutic interventions and monitoring disease progression. However, determining COVID-19 severit...
Humans Neural Networks, Computer COVID-19 Deep Learning Male Tomography, X-Ray Computed Lung Female Severity of Illness Index SARS-CoV-2
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An 8-point scale lung ultrasound scoring network fusing local detail and global features.

Scientific reports Feb 17, 2025
Manual lung ultrasound (LUS) scoring is influenced by clinicians' subjective interpretation, leading to potential inconsistencies and misdiagnoses due to varying levels of experience. To improve monitoring of pulmonary ventilation and support early d...
Lung Diseases Image Interpretation, Computer-Assisted Humans Neural Networks, Computer Lung Deep Learning Ultrasonography
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Point-annotation supervision for robust 3D pulmonary infection segmentation by CT-based cascading deep learning.

Computers in biology and medicine Feb 8, 2025
Infected region segmentation is crucial for pulmonary infection diagnosis, severity assessment, and monitoring treatment progression. High-performance segmentation methods rely heavily on fully annotated, large-scale training datasets. However, manua...
Deep Learning Imaging, Three-Dimensional Humans Tomography, X-Ray Computed Lung
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Adaptive genetic algorithm based deep feature selector for cancer detection in lung histopathological images.

Scientific reports Feb 8, 2025
Cancer is a global health concern because of a significant mortality rate and a wide range of affected organs. Early detection and accurate classification of cancer types are crucial for effective treatment. Imaging tests on different image modalitie...
Genetic Algorithms Deep Learning Image Processing, Computer-Assisted Lung Neoplasms Humans Neural Networks, Computer Algorithms Lung
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Facing Differences of Similarity: Intra- and Inter-Correlation Unsupervised Learning for Chest X-Ray Anomaly Detection.

IEEE transactions on medical imaging Feb 4, 2025
Anomaly detection can significantly aid doctors in interpreting chest X-rays. The commonly used strategy involves utilizing the pre-trained network to extract features from normal data to establish feature representations. However, when a pre-trained...
Humans Algorithms Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic Databases, Factual Lung Unsupervised Machine Learning
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Normative values for lung, bronchial sizes, and bronchus-artery ratios in chest CT scans: from infancy into young adulthood.

European radiology Feb 1, 2025
OBJECTIVE: To estimate the developmental trends of quantitative parameters obtained from chest computed tomography (CT) and to provide normative values on dimensions of bronchi and arteries, as well as bronchus-artery (BA) ratios from preschool age t...
Infant Lung Adolescent Humans Infant, Newborn Tomography, X-Ray Computed Young Adult Radiography, Thoracic Female Male Child, Preschool Bronchi Child Pulmonary Artery Reference Values
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Deep Learning-Assisted Computer-Aided Diagnosis System for Early Detection of Lung Cancer.

Journal of clinical ultrasound : JCU Jan 29, 2025
PURPOSE: The largest cause of cancer-related fatalities worldwide is lung cancer. The dimensions and positioning of the primary tumor, the presence of lesions, the type of lung cancer like malignant or benign, and the good mental health diagnosis all...
Humans Deep Learning Lung Neoplasms Sensitivity and Specificity Neural Networks, Computer Early Detection of Cancer Lung Algorithms Diagnosis, Computer-Assisted
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Advancing lung cancer diagnosis: Combining 3D auto-encoders and attention mechanisms for CT scan analysis.

Journal of X-ray science and technology Jan 28, 2025
ObjectiveThe goal of this study is to assess the effectiveness of a hybrid deep learning model that combines 3D Auto-encoders with attention mechanisms to detect lung cancer early from CT scan images. The study aims to improve diagnostic accuracy, se...
Algorithms Tomography, X-Ray Computed Sensitivity and Specificity Lung Neoplasms Lung Humans Radiographic Image Interpretation, Computer-Assisted Deep Learning Imaging, Three-Dimensional
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Impact of Deep Learning 3D CT Super-Resolution on AI-Based Pulmonary Nodule Characterization.

Tomography (Ann Arbor, Mich.) Jan 27, 2025
BACKGROUND/OBJECTIVES: Correct pulmonary nodule volumetry and categorization is paramount for accurate diagnosis in lung cancer screening programs. CT scanners with slice thicknesses of multiple millimetres are still common worldwide, and slice thick...
Early Detection of Cancer Radiographic Image Interpretation, Computer-Assisted Female Imaging, Three-Dimensional Humans Middle Aged Aged Lung Neoplasms Lung Tomography, X-Ray Computed Male Solitary Pulmonary Nodule Deep Learning
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