Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
[HTML][HTML] A review of deep learning-based detection methods for COVID-19
COVID-19 is a fast-spreading pandemic, and early detection is crucial for stop** the
spread of infection. Lung images are used in the detection of coronavirus infection. Chest X …
spread of infection. Lung images are used in the detection of coronavirus infection. Chest X …
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
Chest X-ray is the first imaging technique that plays an important role in the diagnosis of
COVID-19 disease. Due to the high availability of large-scale annotated image datasets …
COVID-19 disease. Due to the high availability of large-scale annotated image datasets …
[HTML][HTML] A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images
In 2019, the world experienced the rapid outbreak of the Covid-19 pandemic creating an
alarming situation worldwide. The virus targets the respiratory system causing pneumonia …
alarming situation worldwide. The virus targets the respiratory system causing pneumonia …
Coronavirus disease (COVID-19) detection in chest X-ray images using majority voting based classifier ensemble
Abstract Novel coronavirus disease (nCOVID-19) is the most challenging problem for the
world. The disease is caused by severe acute respiratory syndrome coronavirus-2 (SARS …
world. The disease is caused by severe acute respiratory syndrome coronavirus-2 (SARS …
[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 …
Generalist vision foundation models for medical imaging: A case study of segment anything model on zero-shot medical segmentation
Medical image analysis plays an important role in clinical diagnosis. In this paper, we
examine the recent Segment Anything Model (SAM) on medical images, and report both …
examine the recent Segment Anything Model (SAM) on medical images, and report both …
An efficient deep learning approach to pneumonia classification in healthcare
This study proposes a convolutional neural network model trained from scratch to classify
and detect the presence of pneumonia from a collection of chest X‐ray image samples …
and detect the presence of pneumonia from a collection of chest X‐ray image samples …
Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks
Purpose To evaluate the efficacy of deep convolutional neural networks (DCNNs) for
detecting tuberculosis (TB) on chest radiographs. Materials and Methods Four deidentified …
detecting tuberculosis (TB) on chest radiographs. Materials and Methods Four deidentified …
Efficient pneumonia detection in chest xray images using deep transfer learning
Pneumonia causes the death of around 700,000 children every year and affects 7% of the
global population. Chest X-rays are primarily used for the diagnosis of this disease …
global population. Chest X-rays are primarily used for the diagnosis of this disease …