Pulmonology

ECM-CSD: An Efficient Classification Model for Cancer Stage Diagnosis in CT Lung Images Using FCM and SVM Techniques.

As is eminent, lung cancer is one of the death frightening syndromes among people in present cases. The earlier diagnosis and treatment of lung cancer can increase the endurance rate of the affected people. But, the structure of the cancer cell makes the diagnosis process more challenging, in which the most of the cells are superimposed. By adopting the efficient image processing techniques, the d...

An effective and accurate identification system of Mycobacterium tuberculosis using convolution neural networks.

Tuberculosis (TB) remains the leading cause of morbidity and mortality from infectious disease in developing countries. The sputum smear microscopy remains the primary diagnostic laboratory test. However, microscopic examination is always time-consuming and tedious. Therefore, an effective computer-aided image identification system is needed to provide timely assistance in diagnosis. The current i...

Quantifying lung ultrasound comets with a convolutional neural network: Initial clinical results.

Lung ultrasound comets are "comet-tail" artifacts appearing in lung ultrasound images. They are particularly useful in detecting several lung patholog...

Improving Accuracy of Lung Nodule Classification Using Deep Learning with Focal Loss.

Early detection and classification of pulmonary nodules using computer-aided diagnosis (CAD) systems is useful in reducing mortality rates of lung can...

A Comparative Texture Analysis Based on NECT and CECT Images to Differentiate Lung Adenocarcinoma from Squamous Cell Carcinoma.

The purpose of the study was to compare the texture based discriminative performances between non-contrast enhanced computed tomography (NECT) and con...

Pulmonary nodule segmentation with CT sample synthesis using adversarial networks.

PURPOSE: Segmentation of pulmonary nodules is critical for the analysis of nodules and lung cancer diagnosis. We present a novel framework of segmenta...

Gene fingerprint model for literature based detection of the associations among complex diseases: a case study of COPD.

BACKGROUND: Disease comorbidity is very common and has significant impact on disease treatment. Revealing the associations among diseases may help to ...

Automatic 3D Bi-Ventricular Segmentation of Cardiac Images by a Shape-Refined Multi- Task Deep Learning Approach.

Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches h...

Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches.

Risk stratification (characterization) of tumors from radiology images can be more accurate and faster with computer-aided diagnosis (CAD) tools. Tumo...

Predicting radiation pneumonitis in locally advanced stage II-III non-small cell lung cancer using machine learning.

BACKGROUND AND PURPOSE: Radiation pneumonitis (RP) is a radiotherapy dose-limiting toxicity for locally advanced non-small cell lung cancer (LA-NSCLC)...

Detection of pulmonary ground-glass opacity based on deep learning computer artificial intelligence.

BACKGROUND: A deep learning computer artificial intelligence system is helpful for early identification of ground glass opacities (GGOs).

Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers' Health Examination Data.

We aimed to use deep learning to detect tuberculosis in chest radiographs in annual workers' health examination data and compare the performances of c...

Automated analysis of cardiovascular magnetic resonance myocardial native T mapping images using fully convolutional neural networks.

BACKGROUND: Cardiovascular magnetic resonance (CMR) myocardial native T mapping allows assessment of interstitial diffuse fibrosis. In this technique,...

Automatic lung nodule detection using multi-scale dot nodule-enhancement filter and weighted support vector machines in chest computed tomography.

A novel CAD scheme for automated lung nodule detection is proposed to assist radiologists with the detection of lung cancer on CT scans. The proposed ...

Learning to detect chest radiographs containing pulmonary lesions using visual attention networks.

Machine learning approaches hold great potential for the automated detection of lung nodules on chest radiographs, but training algorithms requires ve...

A Large Animal Model of Right Ventricular Failure due to Chronic Thromboembolic Pulmonary Hypertension: A Focus on Function.

Chronic thromboembolic pulmonary hypertension (CTEPH) is a debilitating disease that progresses to right ventricular (RV) failure and death if left un...

A GIS-Based Artificial Neural Network Model for Spatial Distribution of Tuberculosis across the Continental United States.

Despite the usefulness of artificial neural networks (ANNs) in the study of various complex problems, ANNs have not been applied for modeling the geog...

Gallic Acid Potentiates the Antimicrobial Activity of Tulathromycin Against Two Key Bovine Respiratory Disease (BRD) Causing-Pathogens.

Bovine respiratory disease (BRD) is the most common infectious disease in dairy and beef cattle. It is associated with significant morbidity and morta...

Segmentation of lung parenchyma in CT images using CNN trained with the clustering algorithm generated dataset.

BACKGROUND: Lung segmentation constitutes a critical procedure for any clinical-decision supporting system aimed to improve the early diagnosis and tr...

Classification and Quantification of Emphysema Using a Multi-Scale Residual Network.

Automated tissue classification is an essential step for quantitative analysis and treatment of emphysema. Although many studies have been conducted i...