RLDS: An explainable residual learning diagnosis system for fetal congenital heart disease

Qiao, Sibo; Pang, Shanchen; Luo, Gang; Pan, Silin; Yu, Zengchen; Chen, Taotao; Lv, Zhihan

doi:10.1016/j.future.2021.10.001

Cited by 31 publications

(22 citation statements)

References 33 publications

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“…Also, many professionals in the medical field feel uneasy with the neural networks' black box nature and would give more credit for these solutions if they could understand how they have taken decisions. Explainable Artificial Intelligence (XAI) aims to solve these issues by bringing more transparency to the understanding of the model's bias, decisions impact and, as a result, generating better metrics [21].…”

Section: Discussionmentioning

confidence: 99%

Treating Dataset Imbalance in Fetal Echocardiography Classification

Gusmão¹,

Raposo²,

Oliveira³

et al. 2022

Communication Papers of the 17th Conference on Computer Science and Intelligence Systems

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ðAbstract4 Deep learning has been a trending topic during the last few years, notably in medical imaging that employs neural networks for image manipulation, computer-aided detection of diseases, and many other tasks depending on the clinical practices. One possible application that would benefit from these methods is the fetal cardiac view classification, where these different views are useful to obtain valuable information about the patient9s heart development. A trained network could help reduce variance in interpretation and speed up data annotation. Alas, in this context we can face two challenges: datasets may contain a lot of information not relevant to the outcome of the classifier9s training, and the view classes may be unbalanced in the sense that certain classes may have much more samples than others. This paper presents a series of attempts to solve these issues and can be used as a practical guide for training viable classifiers in this context. Index Terms4fetal echocardiography, cardiovascular imaging, image processing, machine learning, data augmentation.

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Section: Discussionmentioning

confidence: 99%

Treating Dataset Imbalance in Fetal Echocardiography Classification

Gusmão¹,

Raposo²,

Oliveira³

et al. 2022

Communication Papers of the 17th Conference on Computer Science and Intelligence Systems

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show abstract

“…To better evaluate the classifier's performance, three Accuracy, recall, and F1 standards are used to assess the model's classification performance [ 19 ]. The F1 score combines precision and recall, a comprehensive assessment of CRANet, so the higher the F1the better CRANet's classification performance.…”

Section: Methodsmentioning

confidence: 99%

“…Jafar et al [18] constructed a hyperparameter-based approach based on ResNet and CNN structures and showed that it resulted in significant performance improvements. Qiao et al [19] proposed a simple and effective residual learning intelligent diagnosis system for diagnosing whether the fetus had congenital heart disease. Ghaderzadeh et al [20].…”

Section: Introductionmentioning

confidence: 99%

CRANet: a comprehensive residual attention network for intracranial aneurysm image classification

et al. 2022

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Rupture of intracranial aneurysm is the first cause of subarachnoid hemorrhage, second only to cerebral thrombosis and hypertensive cerebral hemorrhage, and the mortality rate is very high. MRI technology plays an irreplaceable role in the early detection and diagnosis of intracranial aneurysms and supports evaluating the size and structure of aneurysms. The increase in many aneurysm images, may be a massive workload for the doctors, which is likely to produce a wrong diagnosis. Therefore, we proposed a simple and effective comprehensive residual attention network (CRANet) to improve the accuracy of aneurysm detection, using a residual network to extract the features of an aneurysm. Many experiments have shown that the proposed CRANet model could detect aneurysms effectively. In addition, on the test set, the accuracy and recall rates reached 97.81% and 94%, which significantly improved the detection rate of aneurysms.

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“…Multiple diseases such as cardiovascular, chronic kidney disease (CKD), and diabetes were identified in [7,19,20,21]. Support vector, decision tree, and random forest algorithms were used for classification with a standardized decision support model in [7].…”

Section: Related Workmentioning

confidence: 99%

The Severity Prediction of The Binary And Multi-Class Cardiovascular Disease -- A Machine Learning-Based Fusion Approach

Kibria¹,

Matin²

2022

Preprint

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In today's world, a massive amount of data is available in almost every sector. This data has become an asset as we can use this enormous amount of data to find information. Mainly health care industry contains many data consisting of patient and disease-related information. By using the machine learning technique, we can look for hidden data patterns to predict various diseases. Recently CVDs, or cardiovascular disease, have become a leading cause of death around the world. The number of death due to CVDs is frightening. That is why many researchers are trying their best to design a predictive model that can save many lives using the data mining model. In this research, some fusion models have been constructed to diagnose CVDs along with its severity. Machine learning(ML) algorithms like artificial neural network, SVM, logistic regression, decision tree, random forest, and AdaBoost have been applied to the heart disease dataset to predict disease. Randomoversampler was implemented because of the class imbalance in multiclass classification. To improve the performance of classification, a weighted score fusion approach was taken. At first, the models were trained. After training, two algorithms' decision was combined using a weighted sum rule. A total of three fusion models have been developed from the six ML algorithms. The results were promising in the performance parameter. The proposed approach has been experimented with different test training ratios for binary and multiclass classification problems, and for both of them, the fusion models performed well. The highest accuracy for multiclass classification was found as 75%, and it was 95% for binary. The code can be found in : github/hafsakibria/Weightedscorefusion Keywords Artificial neural network • random forest • decision tree • adaboost • support vector machine • logistic regression • cardiovascular disease • weighted score fusion

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RLDS: An explainable residual learning diagnosis system for fetal congenital heart disease

Cited by 31 publications

References 33 publications

Treating Dataset Imbalance in Fetal Echocardiography Classification

Treating Dataset Imbalance in Fetal Echocardiography Classification

CRANet: a comprehensive residual attention network for intracranial aneurysm image classification

The Severity Prediction of The Binary And Multi-Class Cardiovascular Disease -- A Machine Learning-Based Fusion Approach

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