GAN‐LSTM‐3D: An efficient method for lung tumour 3D reconstruction enhanced by attention‐based LSTM

Hong, Lu; Modirrousta, Mohammad Hossein; Hossein Nasirpour, Mohammad; Mirshekari Chargari, Mohammadreza; Mohammadi, Fardin; Moravvej, Seyed Vahid; Rezvanishad, Leila; Rezvanishad, Mohammadreza; Bakhshayeshi, Ivan; Alizadehsani, Roohallah; Razzak, Imran; Alinejad‐Rokny, Hamid; Nahavandi, Saeid

doi:10.1049/cit2.12223

Cited by 19 publications

(7 citation statements)

References 41 publications

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“…RL is essential in this framework, allowing the model to adjust dynamically to data variability. This flexibility is vital in imbalanced datasets, where healthy instances outnumber myocarditis cases (Hong 2023). RL effectively restructures the problem so that, acting as an agent, the model is motivated to focus more on the minority class.…”

Section: Discussionmentioning

confidence: 99%

ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration

Mirzaee Moghaddam Kasmaee,

Ataei,

Moravvej

et al. 2024

Physiol. Meas.

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Objective. Myocarditis poses a significant health risk, often precipitated by viral infections like Coronavirus disease (COVID-19), and can lead to fatal cardiac complications. As a less invasive alternative to the standard diagnostic practice of endomyocardial biopsy, which is highly invasive and thus limited to severe cases, Cardiac Magnetic Resonance (CMR) imaging offers a promising solution for detecting myocardial abnormalities. Approach. This study introduces a deep model called ELRL-MD that combines ensemble learning and Reinforcement Learning (RL) for effective myocarditis diagnosis from CMR images. The model begins with pre-training via the Artificial Bee Colony (ABC) algorithm to enhance the starting point for learning. An array of Convolutional Neural Networks (CNNs) then works in concert to extract and integrate features from CMR images for accurate diagnosis. Leveraging the Z-Alizadeh Sani myocarditis CMR dataset, the model employs RL to navigate the dataset's imbalance by conceptualizing diagnosis as a decision-making process. Main Results. ELRL-DM demonstrates remarkable efficacy, surpassing other deep learning, conventional machine learning, and transfer learning models, achieving an F-measure of 88.2\% and a geometric mean of 90.6\%. Extensive experimentation helped pinpoint the optimal reward function settings and the perfect count of CNNs. Significance. The study addresses the primary technical challenge of inherent data imbalance in CMR imaging datasets and the risk of models converging on local optima due to suboptimal initial weight settings. Further analysis, leaving out ABC and RL components, confirmed their contributions to the model's overall performance, underscoring the effectiveness of addressing these critical technical challenges.

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Section: Discussionmentioning

confidence: 99%

ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration

Mirzaee Moghaddam Kasmaee,

Ataei,

Moravvej

et al. 2024

Physiol. Meas.

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“…Because of its stratified architecture, deep learning proficiently captures sophisticated features to yield better classification performance, and are increasing being used in numerous applications (Wang et al 2020, Zeng et al 2020, Moravvej et al 2022b. Multilayer perceptron (MLP) is an estimator that was initially developed for nonlinear XOR, and has subsequently been effectively employed to resolve combinatorial optimization issues (Moravvej et al 2021a, Hong et al 2023, finding applications in information processing, pattern recognition, image processing, classification, linear and nonlinear optimization, and real data prediction (Duraković et al 2011, Moravvej et al 2022a. MLP functions as a universal approximation where input signals propagate forward.…”

Section: Introductionmentioning

confidence: 99%

MLP-RL-CRD: diagnosis of cardiovascular risk in athletes using a reinforcement learning-based multilayer perceptron

Bostani,

Mirzaeibonehkhater,

Najafi

et al. 2023

Physiol. Meas.

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Objective. Pre-participation medical screening of athletes is necessary to pinpoint individuals susceptible to cardiovascular events. Approach. The article presents a reinforcement learning (RL)-based multilayer perceptron, termed MLP-RL-CRD, designed to detect cardiovascular risk among athletes. The model underwent training using a publicized dataset that included the anthropological measurements (such as height and weight) and biomedical metrics (covering blood pressure and pulse rate) of 26 002 athletes. To address the data imbalance, a novel RL-based technique was adopted. The problem was framed as a series of sequential decisions in which an agent classified a received instance and received a reward at each level. To resolve the insensitivity to the initialization of conventional gradient-based learning methods, a mutual learning-based artificial bee colony (ML-ABC) was proposed. Main Results. The model outcomes were validated against positive (P) and negative (N) ECG findings that had been labeled by experts to signify individuals ‘at risk’ and ‘not at risk,’ respectively. The MLP-RL-CRD approach achieves superior outcomes (F-measure 87.4%; geometric mean 89.6%) compared with other deep models and traditional machine learning techniques. Optimal values for crucial parameters, including the reward function, were identified for the model based on experiments on the study dataset. Ablation studies, which omitted elements of the suggested model, affirmed the autonomous, positive, stepwise influence of these components on performing the model. Significance. This study introduces a novel, effective method for early cardiovascular risk detection in athletes, merging reinforcement learning and multilayer perceptrons, advancing medical screening and predictive healthcare. The results could have far-reaching implications for athlete health management and the broader field of predictive healthcare analytics.

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“…LSTM is a type of deep network used for various tasks, particularly in handling sequential data [30][31][32]. LSTM networks are well-suited for time-series data such as stock market prediction.…”

Section: Introductionmentioning

confidence: 99%

User2Vec: A Novel Representation for the Information of the Social Networks for Stock Market Prediction Using Convolutional and Recurrent Neural Networks

2023

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Predicting stock market trends is an intriguing and complex problem, which has drawn considerable attention from the research community. In recent years, researchers have employed machine learning techniques to develop prediction models by using numerical market data and textual messages on social networks as their primary sources of information. In this article, we propose User2Vec, a novel approach to improve stock market prediction accuracy, which contributes to more informed investment decision making. User2Vec is a unique method that recognizes the unequal impact of different user opinions on specific stocks, and it assigns weights to these opinions based on the accuracy of their associated social metrics. The User2Vec model begins by encoding each message as a vector. These vectors are then fed into a convolutional neural network (CNN) to generate an aggregated feature vector. Following this, a stacked bi-directional long short-term memory (LSTM) model provides the final representation of the input data over a period. LSTM-based models have shown promising results by effectively capturing the temporal patterns in time series market data. Finally, the output is fed into a classifier that predicts the trend of the target stock price for the next day. In contrast to previous attempts, User2Vec considers not only the sentiment of the messages, but also the social information associated with the users and the text content of the messages. It has been empirically proven that this inclusion provides valuable information for predicting stock direction, thereby significantly enhancing prediction accuracy. The proposed model was rigorously evaluated using various combinations of market data, encoded messages, and social features. The empirical studies conducted on the Dow Jones 30 stock market showed the model’s superiority over existing state-of-the-art models. The findings of these experiments reveal that including social information about users and their tweets, in addition to the sentiment and textual content of their messages, significantly improves the accuracy of stock market prediction.

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GAN‐LSTM‐3D: An efficient method for lung tumour 3D reconstruction enhanced by attention‐based LSTM

Cited by 19 publications

References 41 publications

ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration

ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration

MLP-RL-CRD: diagnosis of cardiovascular risk in athletes using a reinforcement learning-based multilayer perceptron

User2Vec: A Novel Representation for the Information of the Social Networks for Stock Market Prediction Using Convolutional and Recurrent Neural Networks

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