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Lung cancer is the most common of lethal types of cancer. One of the most important and difficult tasks a doctor has to carry out is the detection and diagnosis of cancerous lung nodules from x-ray image's result. Some of these lesions may not be detected because of camouflaged by the underlying anatomical structure, the low-quality of the images or the subjective and variable decision criteria used...
A pulmonary nodule is the most common sign of lung cancer. The proposed system efficiently predicts lung tumor from Computed Tomography (CT) images through image processing techniques coupled with neural network classification as either benign or malignant. The lung CT image is denoised using non-linear total variation algorithm to remove random noise prevalent in CT images. Optimal thresholding is...
To establish the artificial neural network (ANN) model of auxiliary diagnosis of lung cancer combined with tumor markers and picture data collected by bronchofibroscope. The levels of serum carcinoembryonic antigen (CEA), neuron specific enolase (NSE), squamous cell carcinoma antigen (SCC-Ag) and cytokeratin 19 fragment (CYFRA21-1) were detected by enzyme linked immunosorbent assay (ELISA) in 55 lung...
Lung cancer is a material cause of cancer death. To forecast CT diagnosis of lung cancer, this paper proposes a hybrid genetic algorithm-BP neural networks (GA-BP algorithm), which introduces multi-species co-evolution genetic algorithm (MCGA) and simulated annealing algorithm (SA), to solve the problem of traditional GA-BP algorithm and avoid trapping in a local minimum. Experiments indicate that...
In this paper, we propose a non-invasive detection method of lung cancer combined with a sort of virtual SAW gas sensors array and imaging recognition method. A patient's breath goes through an electronic nose with solid phase micro extraction (SPME) and capillary column for pre-concentration and separation of volatile organic compounds (VOCs) respectively, a pair of SAW sensors one coated with a...
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