[HTML][HTML] Hybrid deep learning for detecting lung diseases from X-ray images
Lung disease is common throughout the world. These include chronic obstructive pulmonary
disease, pneumonia, asthma, tuberculosis, fibrosis, etc. Timely diagnosis of lung disease is …
disease, pneumonia, asthma, tuberculosis, fibrosis, etc. Timely diagnosis of lung disease is …
Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data
In this study we developed a graph based semi-supervised learning (SSL) scheme using
deep convolutional neural network (CNN) for breast cancer diagnosis. CNN usually needs a …
deep convolutional neural network (CNN) for breast cancer diagnosis. CNN usually needs a …
Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis
This study aimed to analyze the ability of extracting automatically generated features using
deep structured algorithms in lung nodule CT image diagnosis, and compare its …
deep structured algorithms in lung nodule CT image diagnosis, and compare its …
A novel deep learning framework for lung nodule detection in 3d CT images
R Majidpourkhoei, M Alilou, K Majidzadeh… - Multimedia Tools and …, 2021 - Springer
Lung cancer is one of the deadliest cancers all over the world. One of the indications of lung
cancers is the presence of the lung nodules which can appear individually or attached to the …
cancers is the presence of the lung nodules which can appear individually or attached to the …
Ensemble classification for predicting the malignancy level of pulmonary nodules on chest computed tomography images
N **ao, Y Qiang, MB Zia, S Wang… - Oncology …, 2020 - spandidos-publications.com
Early identification and classification of pulmonary nodules are essential for improving the
survival rates of individuals with lung cancer and are considered to be key requirements for …
survival rates of individuals with lung cancer and are considered to be key requirements for …
An appraisal of nodule diagnosis for lung cancer in CT images
G Zhang, Z Yang, L Gong, S Jiang, L Wang… - Journal of medical …, 2019 - Springer
As “the second eyes” of radiologists, computer-aided diagnosis systems play a significant
role in nodule detection and diagnosis for lung cancer. In this paper, we aim to provide a …
role in nodule detection and diagnosis for lung cancer. In this paper, we aim to provide a …
Automatic lung nodule graph cuts segmentation with deep learning false positive reduction
To automatic detect lung nodules from CT images, we designed a two stage computer aided
detection (CAD) system. The first stage is graph cuts segmentation to identify and segment …
detection (CAD) system. The first stage is graph cuts segmentation to identify and segment …
Similarity measurement of lung masses for medical image retrieval using kernel based semisupervised distance metric
Purpose: To develop a new algorithm to measure the similarity between the query lung
mass and reference lung mass data set for content‐based medical image retrieval (CBMIR) …
mass and reference lung mass data set for content‐based medical image retrieval (CBMIR) …
Balance the nodule shape and surroundings: a new multichannel image based convolutional neural network scheme on lung nodule diagnosis
Deep learning is a trending promising method in medical image analysis area, but how to
efficiently prepare the input image for the deep learning algorithms remains a challenge. In …
efficiently prepare the input image for the deep learning algorithms remains a challenge. In …
A new near-term breast cancer risk prediction scheme based on the quantitative analysis of ipsilateral view mammograms
Purpose To help improve efficacy of screening mammography and eventually establish an
optimal personalized screening paradigm, this study aimed to develop and test a new near …
optimal personalized screening paradigm, this study aimed to develop and test a new near …