Deep learning applications in computed tomography images for pulmonary nodule detection and diagnosis: A review
Lung cancer has one of the highest mortality rates of all cancers and poses a severe threat
to people's health. Therefore, diagnosing lung nodules at an early stage is crucial to …
to people's health. Therefore, diagnosing lung nodules at an early stage is crucial to …
[HTML][HTML] Lung nodule diagnosis and cancer histology classification from computed tomography data by convolutional neural networks: A survey
Lung cancer is among the deadliest cancers. Besides lung nodule classification and
diagnosis, developing non-invasive systems to classify lung cancer histological …
diagnosis, developing non-invasive systems to classify lung cancer histological …
Artificial intelligence in lung cancer imaging: unfolding the future
M Cellina, M Cè, G Irmici, V Ascenti, N Khenkina… - Diagnostics, 2022 - mdpi.com
Lung cancer is one of the malignancies with higher morbidity and mortality. Imaging plays
an essential role in each phase of lung cancer management, from detection to assessment …
an essential role in each phase of lung cancer management, from detection to assessment …
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 …
Artificial intelligence in lung cancer screening: the future is now
M Cellina, LM Cacioppa, M Cè, V Chiarpenello… - Cancers, 2023 - mdpi.com
Simple Summary Lung cancer is a widespread malignant tumour with a high mortality and
morbidity rate and is frequently diagnosed in the middle and late stages when few therapies …
morbidity rate and is frequently diagnosed in the middle and late stages when few therapies …
CCGL-YOLOV5: A cross-modal cross-scale global-local attention YOLOV5 lung tumor detection model
T Zhou, F Liu, X Ye, H Wang, H Lu - Computers in biology and medicine, 2023 - Elsevier
Background Multimodal medical image detection is a key technology in medical image
analysis, which plays an important role in tumor diagnosis. There are different sizes lesions …
analysis, which plays an important role in tumor diagnosis. There are different sizes lesions …
Efficient lung nodule classification using transferable texture convolutional neural network
Lung nodules are vital indicators for the presence of lung cancer. An early detection
enhances the survival rate of the patient by starting treatment at the right time. The detection …
enhances the survival rate of the patient by starting treatment at the right time. The detection …
Deep learning in radiology for lung cancer diagnostics: A systematic review of classification, segmentation, and predictive modeling techniques
This study presents a comprehensive systematic review focusing on the applications of deep
learning techniques in lung cancer radiomics. Through a rigorous screening process of 589 …
learning techniques in lung cancer radiomics. Through a rigorous screening process of 589 …
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 …
Lung nodule classification using deep local–global networks
Purpose Lung nodules have very diverse shapes and sizes, which makes classifying them
as benign/malignant a challenging problem. In this paper, we propose a novel method to …
as benign/malignant a challenging problem. In this paper, we propose a novel method to …