Deep learning techniques to diagnose lung cancer
L Wang - Cancers, 2022 - mdpi.com
Simple Summary This study investigates the latest achievements, challenges, and future
research directions of deep learning techniques for lung cancer and pulmonary nodule …
research directions of deep learning techniques for lung cancer and pulmonary nodule …
The value of artificial intelligence in the diagnosis of lung cancer: A systematic review and meta-analysis
M Liu, J Wu, N Wang, X Zhang, Y Bai, J Guo, L Zhang… - PLoS …, 2023 - journals.plos.org
Lung cancer is a common malignant tumor disease with high clinical disability and death
rates. Currently, lung cancer diagnosis mainly relies on manual pathology section analysis …
rates. Currently, lung cancer diagnosis mainly relies on manual pathology section analysis …
A hybrid deep learning architecture for wind power prediction based on bi-attention mechanism and crisscross optimization
A Meng, S Chen, Z Ou, W Ding, H Zhou, J Fan, H Yin - Energy, 2022 - Elsevier
Accurate wind power forecasting is of great significance for power system operation. In this
study, a triple-stage multi-step wind power forecasting approach is proposed by applying …
study, a triple-stage multi-step wind power forecasting approach is proposed by applying …
A transfer learning approach with a convolutional neural network for the classification of lung carcinoma
Lung cancer is among the most hazardous types of cancer in humans. The correct diagnosis
of pathogenic lung disease is critical for medication. Traditionally, determining the …
of pathogenic lung disease is critical for medication. Traditionally, determining the …
Self-supervised transfer learning based on domain adaptation for benign-malignant lung nodule classification on thoracic CT
H Huang, R Wu, Y Li, C Peng - IEEE Journal of Biomedical and …, 2022 - ieeexplore.ieee.org
The spatial heterogeneity is an important indicator of the malignancy of lung nodules in lung
cancer diagnosis. Compared with 2D nodule CT images, the 3D volumes with entire nodule …
cancer diagnosis. Compared with 2D nodule CT images, the 3D volumes with entire nodule …
A comprehensive review of extreme learning machine on medical imaging
The feedforward neural network based on randomization has been of great interest in the
scientific community, particularly extreme learning machines, due to its simplicity, training …
scientific community, particularly extreme learning machines, due to its simplicity, training …
In situ quality inspection with layer-wise visual images based on deep transfer learning during selective laser melting
Selective laser melting is the most commonly used additive manufacturing technique for
fabricating metal components. However, the SLMed part quality still largely suffered from the …
fabricating metal components. However, the SLMed part quality still largely suffered from the …
Self-supervised transfer learning framework driven by visual attention for benign–malignant lung nodule classification on chest CT
Lung cancer is one of the most fatal malignant diseases, which poses an acute menace to
human health and life. The accurate differential diagnosis of lung nodules is a vital step in …
human health and life. The accurate differential diagnosis of lung nodules is a vital step in …
Detecting prohibited objects with physical size constraint from cluttered X-ray baggage images
X-ray baggage image inspection aims to detect prohibited objects. Existing inspection
systems often rely on humans to scrutinize X-ray images. Although several deep-learning …
systems often rely on humans to scrutinize X-ray images. Although several deep-learning …
Less complexity one-class classification approach using construction error of convolutional image transformation network
One-class classification is a machine learning problem, where training data has only one
class. The objective is to determine if the input data is seen class or unseen class …
class. The objective is to determine if the input data is seen class or unseen class …