Image Segmentation for Mitral Regurgitation with Convolutional Neural Network Based on UNet, Resnet, Vnet, FractalNet and SegNet: A Preliminary Study

Atika, Linda; Nurmaini, Siti; Partan, Radiyati Umi; Sukandi, Erwin

doi:10.3390/bdcc6040141

Cited by 9 publications

(3 citation statements)

References 14 publications

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“…ResNet is a CNN architecture developed by Kaiming He et al in 2015, which introduced the concept of residual connections to address the problem of vanishing gradients in deep networks [138][139][140][141][142][143]. The ResNet architecture consists of multiple residual blocks, each of which includes multiple Conv_Lays and shortcut connections that bypass the Conv_Lays and add the original input to the output of the block.…”

Section: Resnetmentioning

confidence: 99%

Convolutional Neural Networks: A Survey

Krichen

2023

Computers

193

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Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including image recognition, speech recognition, natural language processing (NLP), and even in the field of genomics, where they have been utilized to classify DNA sequences. This paper provides a comprehensive overview of CNNs and their applications in image recognition tasks. It first introduces the fundamentals of CNNs, including the layers of CNNs, convolution operation (Conv_Op), Feat_Maps, activation functions (Activ_Func), and training methods. It then discusses several popular CNN architectures such as LeNet, AlexNet, VGG, ResNet, and InceptionNet, and compares their performance. It also examines when to use CNNs, their advantages and limitations, and provides recommendations for developers and data scientists, including preprocessing the data, choosing appropriate hyperparameters (Hyper_Param), and evaluating model performance. It further explores the existing platforms and libraries for CNNs such as TensorFlow, Keras, PyTorch, Caffe, and MXNet, and compares their features and functionalities. Moreover, it estimates the cost of using CNNs and discusses potential cost-saving strategies. Finally, it reviews recent developments in CNNs, including attention mechanisms, capsule networks, transfer learning, adversarial training, quantization and compression, and enhancing the reliability and efficiency of CNNs through formal methods. The paper is concluded by summarizing the key takeaways and discussing the future directions of CNN research and development.

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Section: Resnetmentioning

confidence: 99%

Convolutional Neural Networks: A Survey

Krichen

2023

Computers

193

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“…On the other hand, in cases with complex images, it often fails to produce satisfactory performance [32]. This is caused by background image factors capable of affecting the segmentation results [33]. Image background factors often arise due to interference from nature, buildings, and others.…”

Section: Introductionmentioning

confidence: 99%

Improving Convective Cloud Classification with Deep Learning: The CC-Unet Model

Rumapea,

Zarlis,

Efendy

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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Analyzing and mitigating natural disasters can be a challenging task, which is why the field of computer science, specifically artificial intelligence (AI) is necessary to aid in the complexity of disaster management. AI provides the tools and analytical models to help solve the intricacies of handling natural disasters. Convective clouds, closely related to rain and can lead to large-scale, prolonged hydrometeorological disasters, are a crucial component to consider. To improve the classification of these clouds, a predictive-analytical model based on deep learning, called the CC-Unet model, was developed. This model utilizes a U-Net architecture and is trained using a dataset of convective cloud images. The researchers used satellite image data from the Himawari 8 satellite collected in May and October 2021. The images were pre-processed and verified using observational data. The model was tested using a random train-test split method, showing that the CC-Unet model had a higher accuracy of 97.29% compared to the U-Net model, which had an accuracy of 94.17%. Additionally, the significance test using the Wilcoxon method showed that the CC-Unet model had significantly different performance results from the U-Net model. The ground truth image was also compared with the predicted image, showing a low root mean square error value of 0.0218, indicating a high level of similarity between the two. Overall, this research demonstrates the potential of AI and deep learning in classifying convective clouds to aid in natural disaster management.

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“…One of the most widely used DL approaches for medical applications is U-shaped encoderdecoder network (UNet) architecture [16][17] [18] [19]. The UNet model is built using convolutional neural network (CNN) layers, which are responsible for learning the features of the input data [17][19] [20].…”

Section: Introductionmentioning

confidence: 99%

XU-NetI: Simple U-Shaped Encoder-Decoder Network for Accurate Imputation of Multivariate Missing Data

Firdaus,

Nurmaini,

Tutuko

et al. 2023

Preprint

Self Cite

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Intensive care unit (ICU) patients often have multiple vital signs monitored continuously. However, missing data is common in ICU settings, negatively impacting clinical decision-making and patient outcomes. In this study, we propose a multivariate data imputation method based on simple U-Shaped encoder-decoder network imputation (XU-NetI) method to learn the underlying patterns in the data and generate imputations for missing values of vital signs data with ICU patients. To evaluate the performance of our imputation methods, we employed a publicly available database such the medical information mart for intensive care III (MIMIC III) v1.4. Our proposed model has been developed to analyze 219.281 vital sign worth of data, focusing on eight essential vital sign features: body temperature, heart rate, respiration rate, systolic blood pressure, diastolic blood pressure, mean blood pressure, oxygen saturation, and glucose. The evaluation results demonstrates the effectiveness of the imputation techniques in improving the accuracy of predictive models. We compared our approach to other state-of-the-art imputation methods including Autoencoder and Convolutional Neural Networks. As a result found, our technique with XU-NetI architecture outperformed them, in terms of root mean square error (RSME) by approximately 0.01, mean absolute error (MAE) by approximately 0.009, and R square (R2) by approximately 0.99. Our method has the potential to enhance clinical decision-making and improve patient outcomes.

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Image Segmentation for Mitral Regurgitation with Convolutional Neural Network Based on UNet, Resnet, Vnet, FractalNet and SegNet: A Preliminary Study

Cited by 9 publications

References 14 publications

Convolutional Neural Networks: A Survey

Convolutional Neural Networks: A Survey

Improving Convective Cloud Classification with Deep Learning: The CC-Unet Model

XU-NetI: Simple U-Shaped Encoder-Decoder Network for Accurate Imputation of Multivariate Missing Data

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