A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning
Y Gu, J Chi, J Liu, L Yang, B Zhang, D Yu… - Computers in biology …, 2021 - Elsevier
Lung cancer has one of the highest mortalities of all cancers. According to the National Lung
Screening Trial, patients who underwent low-dose computed tomography (CT) scanning …
Screening Trial, patients who underwent low-dose computed tomography (CT) scanning …
[HTML][HTML] Overview of radiomics in breast cancer diagnosis and prognostication
Diagnosis of early invasive breast cancer relies on radiology and clinical evaluation,
supplemented by biopsy confirmation. At least three issues burden this approach: a) …
supplemented by biopsy confirmation. At least three issues burden this approach: a) …
VGG19 network assisted joint segmentation and classification of lung nodules in CT images
Pulmonary nodule is one of the lung diseases and its early diagnosis and treatment are
essential to cure the patient. This paper introduces a deep learning framework to support the …
essential to cure the patient. This paper introduces a deep learning framework to support the …
Deep learning in skin disease image recognition: A review
LF Li, X Wang, WJ Hu, NN Xiong, YX Du, BS Li - Ieee Access, 2020 - ieeexplore.ieee.org
The application of deep learning methods to diagnose diseases has become a new
research topic in the medical field. In the field of medicine, skin disease is one of the most …
research topic in the medical field. In the field of medicine, skin disease is one of the most …
Classification of non-small cell lung cancer using one-dimensional convolutional neural network
Abstract Non-Small Cell Lung Cancer (NSCLC) is a major lung cancer type. Proper
diagnosis depends mainly on tumor staging and grading. Pathological prognosis often faces …
diagnosis depends mainly on tumor staging and grading. Pathological prognosis often faces …
2D CNN versus 3D CNN for false-positive reduction in lung cancer screening
J Yu, B Yang, J Wang, J Leader… - Journal of Medical …, 2020 - spiedigitallibrary.org
Purpose: To clarify whether and to what extent three-dimensional (3D) convolutional neural
network (CNN) is superior to 2D CNN when applied to reduce false-positive nodule …
network (CNN) is superior to 2D CNN when applied to reduce false-positive nodule …
[HTML][HTML] Deep learning model as a new trend in computer-aided diagnosis of tumor pathology for lung cancer
L Cong, W Feng, Z Yao, X Zhou, W Xiao - Journal of Cancer, 2020 - ncbi.nlm.nih.gov
Lung cancer is one of the main causes of cancer-related death in the world. The
identification and characteristics of malignant cells are essential for the diagnosis and …
identification and characteristics of malignant cells are essential for the diagnosis and …
An adaptive morphology based segmentation technique for lung nodule detection in thoracic CT image
Lung cancer is one of the most life-threatening cancers mostly indicated by the presence of
nodules in the lung. Doctors and radiological experts use High-Resolution Computed …
nodules in the lung. Doctors and radiological experts use High-Resolution Computed …
Automatic classification of solitary pulmonary nodules in PET/CT imaging employing transfer learning techniques
Early and automatic diagnosis of Solitary Pulmonary Nodules (SPN) in Computed
Tomography (CT) chest scans can provide early treatment for patients with lung cancer, as …
Tomography (CT) chest scans can provide early treatment for patients with lung cancer, as …
Lung nodule classification using biomarkers, volumetric radiomics, and 3D CNNs
We present a hybrid algorithm to estimate lung nodule malignancy that combines imaging
biomarkers from Radiologist's annotation with image classification of CT scans. Our …
biomarkers from Radiologist's annotation with image classification of CT scans. Our …