[HTML][HTML] A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation
Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …
belonging to one class is lower than the other. Ensemble learning combines multiple models …
Bias and class imbalance in oncologic data—towards inclusive and transferrable AI in large scale oncology data sets
Simple Summary Large-scale medical data carries significant areas of underrepresentation
and bias at all levels: clinical, biological, and management. Resulting data sets and outcome …
and bias at all levels: clinical, biological, and management. Resulting data sets and outcome …
DenseNet convolutional neural networks application for predicting COVID-19 using CT image
Recently, the destructive impact of Coronavirus 2019, commonly known as COVID-19, has
affected public health and human lives. This catastrophic effect disrupted human experience …
affected public health and human lives. This catastrophic effect disrupted human experience …
[HTML][HTML] A comparative study on online machine learning techniques for network traffic streams analysis
Modern networks generate a massive amount of traffic data streams. Analyzing this data is
essential for various purposes, such as network resources management and cyber-security …
essential for various purposes, such as network resources management and cyber-security …
SMOTE-NaN-DE: Addressing the noisy and borderline examples problem in imbalanced classification by natural neighbors and differential evolution
J Li, Q Zhu, Q Wu, Z Zhang, Y Gong, Z He… - Knowledge-Based …, 2021 - Elsevier
Learning a classifier from class-imbalance data is an important challenge. Among existing
solutions, SMOTE is one of the most successful methods and has an extensive range of …
solutions, SMOTE is one of the most successful methods and has an extensive range of …
SMOTified-GAN for class imbalanced pattern classification problems
Class imbalance in a dataset is a major problem for classifiers that results in poor prediction
with a high true positive rate (TPR) but a low true negative rate (TNR) for a majority positive …
with a high true positive rate (TPR) but a low true negative rate (TNR) for a majority positive …
UnrollingNet: An attention-based deep learning approach for the segmentation of large-scale point clouds of tunnels
A novel projection-based learning method named UnrollingNet is developed to conduct a
multi-label segmentation of various objects including seepage from 3D point clouds of …
multi-label segmentation of various objects including seepage from 3D point clouds of …
Image enhancement techniques on deep learning approaches for automated diagnosis of COVID-19 features using CXR images
The outbreak of novel coronavirus (COVID-19) disease has infected more than 135.6 million
people globally. For its early diagnosis, researchers consider chest X-ray examinations as a …
people globally. For its early diagnosis, researchers consider chest X-ray examinations as a …
[HTML][HTML] Applications of deep learning in trauma radiology: a narrative review
CT Cheng, CH Ooyang, CH Liao, SC Kang - Biomedical Journal, 2025 - Elsevier
Diagnostic imaging is essential in modern trauma care for initial evaluation and identifying
injuries requiring intervention. Deep learning (DL) has become mainstream in medical …
injuries requiring intervention. Deep learning (DL) has become mainstream in medical …
Multistage transfer learning for medical images
Deep learning is revolutionizing various domains and significantly impacting medical image
analysis. Despite notable progress, numerous challenges remain, necessitating the …
analysis. Despite notable progress, numerous challenges remain, necessitating the …