Comparing Stacking Ensemble Techniques to Improve Musculoskeletal Fracture Image Classification

Kandel, Ibrahem; Castelli, Mauro; Popovič, Aleš

doi:10.3390/jimaging7060100

Cited by 27 publications

(21 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The research employs ensemble machine learning techniques using CNN to classify the bone fracture images with stacking methodology for reliable and robust classification. The result shows that the ensemble method is more reliable and forms a robust output than the manual works from the providers [33].…”

Section: Literature Reviewmentioning

confidence: 94%

Ensemble Machine Learning Algorithms for Prediction and Classification of Medical Images

S.¹,

Daramola²

2021

Artificial Intelligence

View full text Add to dashboard Cite

The employment of machine learning algorithms in disease classification has evolved as a precision medicine for scientific innovation. The geometric growth in various machine learning systems has paved the way for more research in the medical imaging process. This research aims to promote the development of machine learning algorithms for the classification of medical images. Automated classification of medical images is a fascinating application of machine learning and they have the possibility of higher predictability and accuracy. The technological advancement in the processing of medical imaging will help to reduce the complexities of diseases and some existing constraints will be greatly minimized. This research exposes the main ensemble learning techniques as it covers the theoretical background of machine learning, applications, comparison of machine learning and deep learning, ensemble learning with reviews of state-of the art literature, framework, and analysis. The work extends to medical image types, applications, benefits, and operations. We proposed the application of the ensemble machine learning approach in the classification of medical images for better performance and accuracy. The integration of advanced technology in clinical imaging will help in the prompt classification, prediction, early detection, and a better interpretation of medical images, this will, in turn, improves the quality of life and expands the clinical bearing for machine learning applications.

show abstract

Section: Literature Reviewmentioning

confidence: 94%

Ensemble Machine Learning Algorithms for Prediction and Classification of Medical Images

S.¹,

Daramola²

2021

Artificial Intelligence

View full text Add to dashboard Cite

show abstract

“…This study explored the key variables of depressive disorders in female older adults living alone using the stacking ensemble technique. A number of studies (14,15,22) have reported that the stacking ensemble model shows excellent accuracy because it compensates for the overfitting possibility, a disadvantage of a single predictive model. In other words, the goal of the stacking ensemble is to improve generalization capacity, and it has been widely used for classifying and developing predictive models using machine learning.…”

Section: Exploring the Best Predictive Factors For Depressive Disorders Using Stacking Ensemble: Base Modelmentioning

confidence: 99%

“…Therefore, since they all have the same ratio (1 * A + 1 * B + 1 * C), it may increase errors by interfering with random x. In this case, weights are applied in front of each model, and the optimal weight is found by using machine learning, which is presented in Equation (15). The model of Equation ( 15) becomes a classifier with better performance than the model of Equation ( 14) (Error 3 > Error 4).…”

Section: Exploring the Best Predictive Factors For Depressive Disorders Using Stacking Ensemble: Base Modelmentioning

confidence: 99%

“…For the past 10 years, studies in the medical field have continuously tried to identify risk factors for depressive disorders using data mining techniques as well as traditional statistical analysis models (e.g., regression model) (13). Particularly, recent studies (14,15) have used the stacking ensemble technique that predicts y-class by integrating multiple individual machine learning techniques to overcome the limitations of a single machine learning technique. It is known that stacking ensemble has higher accuracy than a single learning machine technique because it predicts again using a meta model based on the data predicted by individual algorithms (14).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Developing a Predictive Model for Depressive Disorders Using Stacking Ensemble and Naive Bayesian Nomogram: Using Samples Representing South Korea

Byeon

2022

Front. Psychiatry

View full text Add to dashboard Cite

This study provided baseline data for preventing depression in female older adults living alone by understanding the degree of their depressive disorders and factors affecting these depressive disorders by analyzing epidemiological survey data representing South Koreans. To achieve the study objective, this study explored the main risk factors of depressive disorders using the stacking ensemble machine technique. Moreover, this study developed a nomogram that could help primary physicians easily interpret high-risk groups of depressive disorders in primary care settings based on the major predictors derived from machine learning. This study analyzed 582 female older adults (≥60 years old) living alone. The depressive disorder, a target variable, was measured using the Korean version of Patient Health Questionnaire-9. This study developed five single predictive models (GBM, Random Forest, Adaboost, SVM, XGBoost) and six stacking ensemble models (GBM + Bayesian regression, RandomForest + Bayesian regression, Adaboost + Bayesian regression, SVM + Bayesian regression, XGBoost + Bayesian regression, GBM + RandomForest + Adaboost + SVM + XGBoost + Bayesian regression) to predict depressive disorders. The naive Bayesian nomogram confirmed that stress perception, subjective health, n-6 fatty acid, n-3 fatty acid, mean hours of sitting per day, and mean daily sleep hours were six major variables related to the depressive disorders of female older adults living alone. Based on the results of this study, it is required to evaluate the multiple risk factors for depression including various measurable factors such as social support.

show abstract

“…Whereas multiple authors provide extensive reviews on general ensemble learning like Ganaiea et al [23], only a handful of works started to survey the deep ensemble learning field. While Cao et al reviewed deep learning based ensemble learning methods specifically in bioinformatics [24], Sagi et al [25], Ju et al [14], and Kandel et al [26] started to provide descriptions or analysis on general deep ensemble learning methods.…”

Section: Introductionmentioning

confidence: 99%

An Analysis on Ensemble Learning Optimized Medical Image Classification With Deep Convolutional Neural Networks

Müller

Soto-Rey

2022

IEEE Access

View full text Add to dashboard Cite

Novel and high-performance medical image classification pipelines are heavily utilizing ensemble learning strategies. The idea of ensemble learning is to assemble diverse models or multiple predictions and, thus, boost prediction performance. However, it is still an open question to what extent as well as which ensemble learning strategies are beneficial in deep learning based medical image classification pipelines. In this work, we proposed a reproducible medical image classification pipeline for analyzing the performance impact of the following ensemble learning techniques: Augmenting, Stacking, and Bagging. The pipeline consists of state-of-the-art preprocessing and image augmentation methods as well as 9 deep convolution neural network architectures. It was applied on four popular medical imaging datasets with varying complexity. Furthermore, 12 pooling functions for combining multiple predictions were analyzed, ranging from simple statistical functions like unweighted averaging up to more complex learning-based functions like support vector machines. Our results revealed that Stacking achieved the largest performance gain of up to 13% F1-score increase. Augmenting showed consistent improvement capabilities by up to 4% and is also applicable to single model based pipelines. Cross-validation based Bagging demonstrated significant performance gain close to Stacking, which resulted in an F1-score increase up to +11%. Furthermore, we demonstrated that simple statistical pooling functions are equal or often even better than more complex pooling functions. We concluded that the integration of ensemble learning techniques is a powerful method for any medical image classification pipeline to improve robustness and boost performance.

show abstract

Comparing Stacking Ensemble Techniques to Improve Musculoskeletal Fracture Image Classification

Cited by 27 publications

References 43 publications

Ensemble Machine Learning Algorithms for Prediction and Classification of Medical Images

Ensemble Machine Learning Algorithms for Prediction and Classification of Medical Images

Developing a Predictive Model for Depressive Disorders Using Stacking Ensemble and Naive Bayesian Nomogram: Using Samples Representing South Korea

An Analysis on Ensemble Learning Optimized Medical Image Classification With Deep Convolutional Neural Networks

Contact Info

Product

Resources

About