[HTML][HTML] Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology
Medical image processing and analysis (also known as Radiomics) is a rapidly growing
discipline that maps digital medical images into quantitative data, with the end goal of …
discipline that maps digital medical images into quantitative data, with the end goal of …
[HTML][HTML] Lung cancer prediction using machine learning and advanced imaging techniques
Abstract Machine learning based lung cancer prediction models have been proposed to
assist clinicians in managing incidental or screen detected indeterminate pulmonary …
assist clinicians in managing incidental or screen detected indeterminate pulmonary …
Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification
We investigate the problem of lung nodule malignancy suspiciousness (the likelihood of
nodule malignancy) classification using thoracic Computed Tomography (CT) images …
nodule malignancy) classification using thoracic Computed Tomography (CT) images …
An interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification
While deep learning methods have demonstrated performance comparable to human
readers in tasks such as computer-aided diagnosis, these models are difficult to interpret, do …
readers in tasks such as computer-aided diagnosis, these models are difficult to interpret, do …
Lung nodule classification using deep features in CT images
Early detection of lung cancer can help in a sharp decrease in the lung cancer mortality rate,
which accounts for more than 17% percent of the total cancer related deaths. A large …
which accounts for more than 17% percent of the total cancer related deaths. A large …
Assessing the accuracy of a deep learning method to risk stratify indeterminate pulmonary nodules
PP Massion, S Antic, S Ather, C Arteta… - American journal of …, 2020 - atsjournals.org
Rationale: The management of indeterminate pulmonary nodules (IPNs) remains
challenging, resulting in invasive procedures and delays in diagnosis and treatment …
challenging, resulting in invasive procedures and delays in diagnosis and treatment …
Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine
H Madero Orozco, OO Vergara Villegas… - Biomedical engineering …, 2015 - Springer
Background Lung cancer is a leading cause of death worldwide; it refers to the uncontrolled
growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the …
growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the …
An attention-enhanced cross-task network to analyse lung nodule attributes in CT images
Accurate characterization of visual attributes such as spiculation, lobulation, and calcification
of lung nodules in computed tomography (CT) images is critical in cancer management. The …
of lung nodules in computed tomography (CT) images is critical in cancer management. The …
Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database …
MC Hancock, JF Magnan - Journal of Medical Imaging, 2016 - spiedigitallibrary.org
In the assessment of nodules in CT scans of the lungs, a number of image-derived features
are diagnostically relevant. Currently, many of these features are defined only qualitatively …
are diagnostically relevant. Currently, many of these features are defined only qualitatively …
[HTML][HTML] Lung cancer detection using probabilistic neural network with modified crow-search algorithm
SC SR, H Rajaguru - Asian Pacific journal of cancer prevention …, 2019 - ncbi.nlm.nih.gov
Objective: Lung cancer is a type of malignancy that occurs most commonly among men and
the third most common type of malignancy among women. The timely recognition of lung …
the third most common type of malignancy among women. The timely recognition of lung …