Deep learning techniques to diagnose lung cancer
L Wang - Cancers, 2022 - mdpi.com
Simple Summary This study investigates the latest achievements, challenges, and future
research directions of deep learning techniques for lung cancer and pulmonary nodule …
research directions of deep learning techniques for lung cancer and pulmonary nodule …
A survey on cancer detection via convolutional neural networks: Current challenges and future directions
Cancer is a condition in which abnormal cells uncontrollably split and damage the body
tissues. Hence, detecting cancer at an early stage is highly essential. Currently, medical …
tissues. Hence, detecting cancer at an early stage is highly essential. Currently, medical …
Inf-net: Automatic covid-19 lung infection segmentation from ct images
Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to
face an existential health crisis. Automated detection of lung infections from computed …
face an existential health crisis. Automated detection of lung infections from computed …
Unet++: Redesigning skip connections to exploit multiscale features in image segmentation
The state-of-the-art models for medical image segmentation are variants of U-Net and fully
convolutional networks (FCN). Despite their success, these models have two limitations:(1) …
convolutional networks (FCN). Despite their success, these models have two limitations:(1) …
[HTML][HTML] Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework
The advancement of artificial intelligence concurrent with the development of medical
imaging techniques provided a unique opportunity to turn medical imaging from mostly …
imaging techniques provided a unique opportunity to turn medical imaging from mostly …
FSS-2019-nCov: A deep learning architecture for semi-supervised few-shot segmentation of COVID-19 infection
M Abdel-Basset, V Chang, H Hawash… - Knowledge-Based …, 2021 - Elsevier
The newly discovered coronavirus (COVID-19) pneumonia is providing major challenges to
research in terms of diagnosis and disease quantification. Deep-learning (DL) techniques …
research in terms of diagnosis and disease quantification. Deep-learning (DL) techniques …
Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R‐CNN
Purpose Automatic breast ultrasound (ABUS) imaging has become an essential tool in
breast cancer diagnosis since it provides complementary information to other imaging …
breast cancer diagnosis since it provides complementary information to other imaging …
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 …
Radiomics in breast MRI: Current progress toward clinical application in the era of artificial intelligence
Breast magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast
cancer diagnosis and is widely used clinically. Dynamic contrast-enhanced MRI is the basis …
cancer diagnosis and is widely used clinically. Dynamic contrast-enhanced MRI is the basis …
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 …