Unsupervised domain adaptation for Covid-19 classification based on balanced slice Wasserstein distance
J Gu, X Qian, Q Zhang, H Zhang, F Wu - Computers in Biology and …, 2023 - Elsevier
Covid-19 has swept the world since 2020, taking millions of lives. In order to seek a rapid
diagnosis of Covid-19, deep learning-based Covid-19 classification methods have been …
diagnosis of Covid-19, deep learning-based Covid-19 classification methods have been …
Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis
T Sugibayashi, SL Walston… - European …, 2023 - Eur Respiratory Soc
Background Deep learning (DL), a subset of artificial intelligence (AI), has been applied to
pneumothorax diagnosis to aid physician diagnosis, but no meta-analysis has been …
pneumothorax diagnosis to aid physician diagnosis, but no meta-analysis has been …
[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 …
Diagnosis and multi-classification of lung diseases in CXR images using optimized deep convolutional neural network
A deep learning (DL) architecture is proposed in this study for the multi-class classification of
COVID-19, lung opacity, lung cancer, tuberculosis (TB), and pneumonia. There are two …
COVID-19, lung opacity, lung cancer, tuberculosis (TB), and pneumonia. There are two …
Analysis of diabetic retinopathy (DR) based on the deep learning
If Diabetic Retinopathy (DR) patients do not receive quick diagnosis and treatment, they may
lose vision. DR, an eye disorder caused by high blood glucose, is becoming more prevalent …
lose vision. DR, an eye disorder caused by high blood glucose, is becoming more prevalent …
Automated COVID-19 detection with convolutional neural networks
A Dumakude, AE Ezugwu - Scientific Reports, 2023 - nature.com
This paper focuses on addressing the urgent need for efficient and accurate automated
screening tools for COVID-19 detection. Inspired by existing research efforts, we propose …
screening tools for COVID-19 detection. Inspired by existing research efforts, we propose …
Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers
K Chadaga, S Prabhu, N Sampathila, R Chadaga… - Scientific Reports, 2024 - nature.com
The COVID-19 influenza emerged and proved to be fatal, causing millions of deaths
worldwide. Vaccines were eventually discovered, effectively preventing the severe …
worldwide. Vaccines were eventually discovered, effectively preventing the severe …
Automated breast tumor ultrasound image segmentation with hybrid UNet and classification using fine-tuned CNN model
Introduction Breast cancer stands as the second most deadly form of cancer among women
worldwide. Early diagnosis and treatment can significantly mitigate mortality rates. Purpose …
worldwide. Early diagnosis and treatment can significantly mitigate mortality rates. Purpose …
[HTML][HTML] An explainable artificial intelligence model for multiple lung diseases classification from chest X-ray images using fine-tuned transfer learning
Traditional deep learning models are often considered “black boxes” due to their lack of
interpretability, which limits their therapeutic use despite their success in classification tasks …
interpretability, which limits their therapeutic use despite their success in classification tasks …
An efficient SMD-PCBA detection based on YOLOv7 network model
Z Li, J Yan, J Zhou, X Fan, J Tang - Engineering Applications of Artificial …, 2023 - Elsevier
Abstract Modern Printed Circuit Board Assembly (PCBA) manufacturing processes require
more accurate and robust defect inspection methods. Despite the potential of deep learning …
more accurate and robust defect inspection methods. Despite the potential of deep learning …