AIMC Topic: Tomography, X-Ray Computed

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An automated two-stage approach to kidney and tumor segmentation in CT imaging.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2024
BACKGROUND: The incidence of kidney tumors is progressively increasing each year. The precision of segmentation for kidney tumors is crucial for diagnosis and treatment.
Humans Neural Networks, Computer Algorithms Kidney Tomography, X-Ray Computed Kidney Neoplasms Deep Learning Image Processing, Computer-Assisted
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A Dynamic Context Encoder Network for Liver Tumor Segmentation.

Current medical imaging Jan 1, 2024
BACKGROUND: Accurate segmentation of liver tumor regions in medical images is of great significance for clinical diagnosis and the planning of surgical treatments. Recent advancements in machine learning have shown that convolutional neural networks ...
Image Processing, Computer-Assisted Humans Tomography, X-Ray Computed Liver Neoplasms Neural Networks, Computer Machine Learning
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FDB-Net: Fusion double branch network combining CNN and transformer for medical image segmentation.

Journal of X-ray science and technology Jan 1, 2024
BACKGROUND: The rapid development of deep learning techniques has greatly improved the performance of medical image segmentation, and medical image segmentation networks based on convolutional neural networks and Transformer have been widely used in ...
Algorithms Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted Humans Neural Networks, Computer
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DKCNN: Improving deep kernel convolutional neural network-based COVID-19 identification from CT images of the chest.

Journal of X-ray science and technology Jan 1, 2024
BACKGROUND: An efficient deep convolutional neural network (DeepCNN) is proposed in this article for the classification of Covid-19 disease.
COVID-19 Algorithms Tomography, X-Ray Computed SARS-CoV-2 Thorax Humans Betacoronavirus Neural Networks, Computer Pneumonia, Viral Pandemics Deep Learning Image Processing, Computer-Assisted Coronavirus Infections
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Radiomics and Clinical Characters Based Gaussian Naive Bayes (GNB) Model for Preoperative Differentiation of Pulmonary Pure Invasive Mucinous Adenocarcinoma From Mixed Mucinous Adenocarcinoma.

Technology in cancer research & treatment Jan 1, 2024
To develop and validate predictive models based on clinical parameters, and radiomic features to distinguish pulmonary pure invasive mucinous adenocarcinoma (pIMA) from mixed mucinous adenocarcinoma (mIMA) before surgery. From January 2017 to Decem...
Machine Learning Algorithms Retrospective Studies Aged Radiomics Tomography, X-Ray Computed Female Lung Neoplasms Humans Diagnosis, Differential Bayes Theorem Adenocarcinoma, Mucinous Male Middle Aged ROC Curve
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Deep-Learning Model Prediction of Radiation Pneumonitis Using Pretreatment Chest Computed Tomography and Clinical Factors.

Technology in cancer research & treatment Jan 1, 2024
This study aimed to build a comprehensive deep-learning model for the prediction of radiation pneumonitis using chest computed tomography (CT), clinical, dosimetric, and laboratory data. Radiation therapy is an effective tool for treating patients ...
Support Vector Machine Radiotherapy Dosage Radiation Pneumonitis Middle Aged Deep Learning ROC Curve Lung Neoplasms Humans Aged, 80 and over Neural Networks, Computer Adult Prognosis Male Female Aged Retrospective Studies Tomography, X-Ray Computed
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A Novel Detection of Cerebrovascular Disease using Multimodal Medical Image Fusion.

Recent advances in inflammation & allergy drug discovery Jan 1, 2024
BACKGROUND: Diseases are medical situations that are allied with specific signs and symptoms. A disease may be instigated by internal dysfunction or external factors like pathogens. Cerebrovascular disease can progress from diverse causes, comprising...
Cerebrovascular Disorders Fourier Analysis Magnetic Resonance Imaging Multimodal Imaging Principal Component Analysis Tomography, X-Ray Computed Support Vector Machine Humans Machine Learning
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Predicting patient-specific organ doses from thoracic CT examinations using support vector regression algorithm.

Journal of X-ray science and technology Jan 1, 2024
PURPOSE: This study aims to propose and develop a fast, accurate, and robust prediction method of patient-specific organ doses from CT examinations using minimized computational resources.
Humans Algorithms Adult Male Middle Aged Lung Tomography, X-Ray Computed Female Aged Monte Carlo Method Support Vector Machine Radiation Dosage Radiography, Thoracic
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A user-friendly deep learning application for accurate lung cancer diagnosis.

Journal of X-ray science and technology Jan 1, 2024
BACKGROUND: Accurate diagnosis and subsequent delineated treatment planning require the experience of clinicians in the handling of their case numbers. However, applying deep learning in image processing is useful in creating tools that promise faste...
Lung Neoplasms Aged Male Middle Aged Imaging, Three-Dimensional Image Processing, Computer-Assisted Lung Deep Learning Humans Tomography, X-Ray Computed Female
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CT-based intratumoral and peritumoral deep transfer learning features prediction of lymph node metastasis in non-small cell lung cancer.

Journal of X-ray science and technology Jan 1, 2024
BACKGROUND: The main metastatic route for lung cancer is lymph node metastasis, and studies have shown that non-small cell lung cancer (NSCLC) has a high risk of lymph node infiltration.
Tomography, X-Ray Computed Female Aged Adult Lung Neoplasms Male Lymph Nodes Humans Middle Aged Carcinoma, Non-Small-Cell Lung Retrospective Studies Lymphatic Metastasis Deep Learning
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