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] Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: An overview
J Gao, Q Jiang, B Zhou, D Chen - Mathematical Biosciences and …, 2019 - aimspress.com
Computer-aided detection or diagnosis (CAD) has been a promising area of research over
the last two decades. Medical image analysis aims to provide a more efficient diagnostic and …
the last two decades. Medical image analysis aims to provide a more efficient diagnostic and …
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem
J Hofmanninger, F Prayer, J Pan, S Röhrich… - European Radiology …, 2020 - Springer
Background Automated segmentation of anatomical structures is a crucial step in image
analysis. For lung segmentation in computed tomography, a variety of approaches exists …
analysis. For lung segmentation in computed tomography, a variety of approaches exists …
Modality specific U-Net variants for biomedical image segmentation: a survey
With the advent of advancements in deep learning approaches, such as deep convolution
neural network, residual neural network, adversarial network; U-Net architectures are most …
neural network, residual neural network, adversarial network; U-Net architectures are most …
A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models
Pneumonia is one of the diseases that people may encounter in any period of their lives.
Approximately 18% of infectious diseases are caused by pneumonia. This disease may …
Approximately 18% of infectious diseases are caused by pneumonia. This disease may …
Et-net: A generic edge-attention guidance network for medical image segmentation
Segmentation is a fundamental task in medical image analysis. However, most existing
methods focus on primary region extraction and ignore edge information, which is useful for …
methods focus on primary region extraction and ignore edge information, which is useful for …
A deep Residual U-Net convolutional neural network for automated lung segmentation in computed tomography images
To improve the early diagnosis and treatment of lung diseases automated lung
segmentation from CT images is a crucial task for clinical decision. The segmentation of the …
segmentation from CT images is a crucial task for clinical decision. The segmentation of the …
Detection of COVID-19 cases through X-ray images using hybrid deep neural network
Purpose The latest 2019 coronavirus (COVID-2019), which first appeared in December
2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It …
2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It …
Review of deep learning based automatic segmentation for lung cancer radiotherapy
X Liu, KW Li, R Yang, LS Geng - Frontiers in oncology, 2021 - frontiersin.org
Lung cancer is the leading cause of cancer-related mortality for males and females.
Radiation therapy (RT) is one of the primary treatment modalities for lung cancer. While …
Radiation therapy (RT) is one of the primary treatment modalities for lung cancer. While …
Machine learning in ultrasound computer‐aided diagnostic systems: a survey
The ultrasound imaging is one of the most common schemes to detect diseases in the
clinical practice. There are many advantages of ultrasound imaging such as safety …
clinical practice. There are many advantages of ultrasound imaging such as safety …