Dense convolutional network and its application in medical image analysis
T Zhou, XY Ye, HL Lu, X Zheng, S Qiu… - BioMed Research …, 2022 - Wiley Online Library
Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent
years, which has good applications in medical image analysis. In this paper, DenseNet is …
years, which has good applications in medical image analysis. In this paper, DenseNet is …
Artificial intelligence in medicine: where are we now?
S Kulkarni, N Seneviratne, MS Baig, AHA Khan - Academic radiology, 2020 - Elsevier
Artificial intelligence in medicine has made dramatic progress in recent years. However,
much of this progress is seemingly scattered, lacking a cohesive structure for the discerning …
much of this progress is seemingly scattered, lacking a cohesive structure for the discerning …
Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images
Computer-aided diagnosis for the reliable and fast detection of coronavirus disease (COVID-
19) has become a necessity to prevent the spread of the virus during the pandemic to ease …
19) has become a necessity to prevent the spread of the virus during the pandemic to ease …
[HTML][HTML] High-precision multiclass classification of lung disease through customized MobileNetV2 from chest X-ray images
In this study, multiple lung diseases are diagnosed with the help of the Neural Network
algorithm. Specifically, Emphysema, Infiltration, Mass, Pleural Thickening, Pneumonia …
algorithm. Specifically, Emphysema, Infiltration, Mass, Pleural Thickening, Pneumonia …
Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study
Background There is interest in using convolutional neural networks (CNNs) to analyze
medical imaging to provide computer-aided diagnosis (CAD). Recent work has suggested …
medical imaging to provide computer-aided diagnosis (CAD). Recent work has suggested …
Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study
Background Non-contrast head CT scan is the current standard for initial imaging of patients
with head trauma or stroke symptoms. We aimed to develop and validate a set of deep …
with head trauma or stroke symptoms. We aimed to develop and validate a set of deep …
Deep-learning framework to detect lung abnormality–A study with chest X-Ray and lung CT scan images
Lung abnormalities are highly risky conditions in humans. The early diagnosis of lung
abnormalities is essential to reduce the risk by enabling quick and efficient treatment. This …
abnormalities is essential to reduce the risk by enabling quick and efficient treatment. This …
Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning
A large number of children die due to pneumonia every year worldwide. An estimated 1.2
million episodes of pneumonia were reported in children up to 5 years of age, of which …
million episodes of pneumonia were reported in children up to 5 years of age, of which …
Deep learning-based meta-classifier approach for COVID-19 classification using CT scan and chest X-ray images
Literature survey shows that convolutional neural network (CNN)-based pretrained models
have been largely used for CoronaVirus Disease 2019 (COVID-19) classification using …
have been largely used for CoronaVirus Disease 2019 (COVID-19) classification using …
Chest X-ray analysis empowered with deep learning: A systematic review
Chest radiographs are widely used in the medical domain and at present, chest X-radiation
particularly plays an important role in the diagnosis of medical conditions such as …
particularly plays an important role in the diagnosis of medical conditions such as …