Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET
Medical imaging is a rich source of invaluable information necessary for clinical judgements.
However, the analysis of those exams is not a trivial assignment. In recent times, the use of …
However, the analysis of those exams is not a trivial assignment. In recent times, the use of …
Strengths, weaknesses, opportunities, and threats analysis of artificial intelligence and machine learning applications in radiology
Currently, the use of artificial intelligence (AI) in radiology, particularly machine learning
(ML), has become a reality in clinical practice. Since the end of the last century, several ML …
(ML), has become a reality in clinical practice. Since the end of the last century, several ML …
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 …
A review of deep learning techniques for lung cancer screening and diagnosis based on CT images
One of the most common and deadly diseases in the world is lung cancer. Only early
identification of lung cancer can increase a patient's probability of survival. A frequently used …
identification of lung cancer can increase a patient's probability of survival. A frequently used …
A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence
Abstract COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million
fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute …
fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute …
Detection and classification of pulmonary nodules using convolutional neural networks: a survey
CT screening has been proven to be effective for diagnosing lung cancer at its early
manifestation in the form of pulmonary nodules, thus decreasing the mortality. However, the …
manifestation in the form of pulmonary nodules, thus decreasing the mortality. However, the …
Radiomics: a primer on high-throughput image phenoty**
Radiomics is a high-throughput approach to image phenoty**. It uses computer
algorithms to extract and analyze a large number of quantitative features from radiological …
algorithms to extract and analyze a large number of quantitative features from radiological …
Lung computed tomography image segmentation based on U-Net network fused with dilated convolution
K Chen, Y Xuan, A Lin, S Guo - Computer Methods and Programs in …, 2021 - Elsevier
Purpose In order to solve the problem of accurate and effective segmentation of the patient's
lung computed tomography (CT) images, so as to improve the efficiency of treating lung …
lung computed tomography (CT) images, so as to improve the efficiency of treating lung …
An effective approach for CT lung segmentation using mask region-based convolutional neural networks
Q Hu, LFF Souza, GB Holanda, SSA Alves… - Artificial intelligence in …, 2020 - Elsevier
Computer vision systems have numerous tools to assist in various medical fields, notably in
image diagnosis. Computed tomography (CT) is the principal imaging method used to assist …
image diagnosis. Computed tomography (CT) is the principal imaging method used to assist …
An automated slice sorting technique for multi-slice computed tomography liver cancer images using convolutional network
An early detection and diagnosis of liver cancer can help the radiation therapist in choosing
the target area and the amount of radiation dose to be delivered to the patients. The …
the target area and the amount of radiation dose to be delivered to the patients. The …