Automatic pulmonary nodule detection applying deep learning or machine learning algorithms to the LIDC-IDRI database: a systematic review
LM Pehrson, MB Nielsen, C Ammitzbøl Lauridsen - Diagnostics, 2019 - mdpi.com
The aim of this study was to provide an overview of the literature available on machine
learning (ML) algorithms applied to the Lung Image Database Consortium Image Collection …
learning (ML) algorithms applied to the Lung Image Database Consortium Image Collection …
Lung nodules detection using semantic segmentation and classification with optimal features
Lung cancer is a deadly disease if not diagnosed in its early stages. However, early
detection of lung cancer is a challenging task due to the shape and size of its nodules …
detection of lung cancer is a challenging task due to the shape and size of its nodules …
Automated pulmonary nodule classification and detection using deep learning architectures
Recent advancement in biomedical imaging technologies has contributed to tremendous
opportunities for the health care sector and the biomedical community. However, collecting …
opportunities for the health care sector and the biomedical community. However, collecting …
Features processing for random forest optimization in lung nodule localization
NS El-Askary, MAM Salem, MI Roushdy - Expert Systems with Applications, 2022 - Elsevier
Lung nodule can cause lung cancer and so researchers do their best to detect those
nodules in their early stages. Machine learning algorithms are used to detect lung nodules …
nodules in their early stages. Machine learning algorithms are used to detect lung nodules …
A 3D nodule candidate detection method supported by hybrid features to reduce false positives in lung nodule detection
Lungs cancer is a fatal disease. However, its early detection increases the chances of
survival among patients. An automated nodule detection system provides the second …
survival among patients. An automated nodule detection system provides the second …
Efficacy of exponentiation method with a convolutional neural network for classifying lung nodules on CT images by malignancy level
T Usuzaki, K Takahashi, H Takagi, M Ishikuro… - European …, 2023 - Springer
Objectives The aim of this study was to examine the performance of a convolutional neural
network (CNN) combined with exponentiating each pixel value in classifying benign and …
network (CNN) combined with exponentiating each pixel value in classifying benign and …
Human motion gesture recognition based on computer vision
R Ma, Z Zhang, E Chen - Complexity, 2021 - Wiley Online Library
Human motion gesture recognition is the most challenging research direction in the field of
computer vision, and it is widely used in human‐computer interaction, intelligent monitoring …
computer vision, and it is widely used in human‐computer interaction, intelligent monitoring …
A survey on lung CT datasets and research trends
RV Adiraju, S Elias - Research on Biomedical Engineering, 2021 - Springer
Purpose Lung cancer is the most dangerous of all forms of cancer and it has the highest
occurrence rate, world over. Early detection of lung cancer is a difficult task. Medical images …
occurrence rate, world over. Early detection of lung cancer is a difficult task. Medical images …
Lung nodules detection using semantic segmentation and classification with optimal features
Lung cancer is a deadly disease if not diagnosed in its early stages. However, early
detection of lung cancer is a challenging task due to the shape and size of its nodules …
detection of lung cancer is a challenging task due to the shape and size of its nodules …
[PDF][PDF] Classification of benign and malignant pulmonary nodules in ldct images using radiomic features
Cancer is a cause of premature death in humans. Cancer-related deaths are increasing
worldwide over the years. Among all cancers, lung cancer contributes to a major proportion …
worldwide over the years. Among all cancers, lung cancer contributes to a major proportion …