An Efficient DA-Net Architecture for Lung Nodule Segmentation

Maqsood, Muazzam; Yasmin, Sadaf; Mehmood, Irfan; Bukhari, Maryam; Kim, Mucheol

doi:10.3390/math9131457

Cited by 42 publications

(17 citation statements)

References 69 publications

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“…With the current advancements in deep learning, there has been a complete shift toward using these models for data analytics. The results of deep learning-based models are exceptional for classification and prediction purposes [73,74]. From this perceptive, the current work has employed a deep learning model, namely, a convolutional neural network (CNN), to carry out social media sentiment analysis with regard to the Brexit event, to assess its impact on stock exchange markets.…”

Section: Discussionmentioning

confidence: 99%

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Analyzing the Stock Exchange Markets of EU Nations: A Case Study of Brexit Social Media Sentiment

Maqsood

Yasmin

et al. 2022

Systems

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Stock exchange analysis is regarded as a stochastic and demanding real-world setting in which fluctuations in stock prices are influenced by a wide range of aspects and events. In recent years, there has been a great deal of interest in social media-based data analytics for analyzing stock exchange markets. This is due to the fact that the sentiments around major global events like Brexit or COVID-19 significantly affect business decisions and investor perceptions, as well as transactional trading statistics and index values. Hence, in this research, we examined a case study from the Brexit event to assess the influence that feelings on the subject have had on the stock markets of European Union (EU) nations. Brexit has implications for Britain and other countries under the umbrella of the European Union (EU). However, a common point of debate is the EU’s contribution preferences and benefit imbalance. For this reason, the Brexit event and its impact on stock markets for major contributors and countries with minimum donations need to be evaluated accurately. As a result, to achieve accurate analysis of the stock exchanges of different EU nations from two different viewpoints, i.e., the major contributors and countries contributing least, in response to the Brexit event, we suggest an optimal deep learning and machine learning model that incorporates social media sentiment analysis regarding Brexit to perform stock market prediction. More precisely, the machine learning-based models include support vector machines (SVM) and linear regression (LR), while convolutional neural networks (CNNs) are used as a deep learning model. In addition, this method incorporates around 1.82 million tweets regarding the major contributors and countries contributing least to the EU budget. The findings show that sentiment analysis of Brexit events using a deep learning model delivers better results in comparison with machine learning models, in terms of root mean square values (RMSE). The outcomes of stock exchange analysis for the least contributing nations in relation to the Brexit event can aid them in making stock market judgments that will eventually benefit their country and improve their poor economies. Likewise, the results of stock exchange analysis for major contributing nations can assist in lowering the possibility of loss in relation to investments, as well as helping them to make effective decisions.

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Section: Discussionmentioning

confidence: 99%

“…Deep learning models are used to achieve better results in different application areas such as medical imaging [73,74], biometric systems [75], as well as natural language-based tasks [76]. In addition, as compared to linear regression or support vector regression models, they perform better.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Analyzing the Stock Exchange Markets of EU Nations: A Case Study of Brexit Social Media Sentiment

Maqsood

Yasmin

et al. 2022

Systems

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“…Other than traditional machine learning methods, some researchers have also proposed deep learning-based advanced algorithms in the financial domain of stock data because these techniques perform well in diverse domains [30][31][32]. For example, Hiransha et al [33] proposed four kinds of deep neural networks, namely, MLP, recurrent neural networks, long-short-term memory (LSTM) networks, and convolutional neural networks (CNNs) for stock market predictions using historical data.…”

Section: Stock Market Analysis Using Deep Learning Methodsmentioning

confidence: 99%

An Empirical Study of Macroeconomic Factors and Stock Returns in the Context of Economic Uncertainty News Sentiment Using Machine Learning

et al. 2022

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Stock markets accurately reflect countries’ economic health, and stock returns are tightly related to economic indices. One popular area of financial research is the factors that influence stock returns. Several investigations have frequently cited macroeconomic factors, among numerous elements. Therefore, this study focuses on the empirical analysis of the relationship between macroeconomic factors and stock market returns. When a stock market becomes increasingly volatile, it becomes susceptible to economic uncertainty news, and information on social media platforms. Thus, we incorporated a new dimension of economic uncertainty news sentiment (EUNS) for stock return predictions. We employed the daily data ofgold index, crude oil price, interest rate, exchange rate, and stock returns for a set of countries from January 2010 to December 2020. Subsequently, to compute coefficients, we conducted a regression analysis using one of the more sophisticated approaches: single-layer neural networks and ordinary least square regression. In addition, we only computed EUNS for the period of the fiscal budget announcement for the US, Turkey, and Hong Kong. The results indicate that the gold index, interest rate, and exchange rate are highly significant and negative macroeconomic factors for all analyzed countries. These findings also indicate that EUNS is important and detrimental for projecting stock returns.

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“…Furthermore, deep learning methods focusing on multi-scale have also been applied to lung nodule segmentation. For example, Maqsood et al [ 33 ] proposed a U-Net-based segmentation framework that integrates dense deep blocks and dense Atrous blocks. Shi et al [ 34 ] presented a lung nodule segmentation model multi-scale residual U-Net (MCA-ResUNet), which applies Atrous Spatial Pyramid Pooling (ASPP) as a bridging module and adds three adjacent smaller-scale guided Layer-crossed Context Attention (LCA) mechanisms.…”

Section: Related Workmentioning

confidence: 99%

Improved Complementary Pulmonary Nodule Segmentation Model Based on Multi-Feature Fusion

Tang

Jiang

et al. 2022

Entropy

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Accurate segmentation of lung nodules from pulmonary computed tomography (CT) slices plays a vital role in the analysis and diagnosis of lung cancer. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in the automatic segmentation of lung nodules. However, they are still challenged by the large diversity of segmentation targets, and the small inter-class variances between the nodule and its surrounding tissues. To tackle this issue, we propose a features complementary network according to the process of clinical diagnosis, which made full use of the complementarity and facilitation among lung nodule location information, global coarse area, and edge information. Specifically, we first consider the importance of global features of nodules in segmentation and propose a cross-scale weighted high-level feature decoder module. Then, we develop a low-level feature decoder module for edge feature refinement. Finally, we construct a complementary module to make information complement and promote each other. Furthermore, we weight pixels located at the nodule edge on the loss function and add an edge supervision to the deep supervision, both of which emphasize the importance of edges in segmentation. The experimental results demonstrate that our model achieves robust pulmonary nodule segmentation and more accurate edge segmentation.

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An Efficient DA-Net Architecture for Lung Nodule Segmentation

Cited by 42 publications

References 69 publications

Analyzing the Stock Exchange Markets of EU Nations: A Case Study of Brexit Social Media Sentiment

Analyzing the Stock Exchange Markets of EU Nations: A Case Study of Brexit Social Media Sentiment

An Empirical Study of Macroeconomic Factors and Stock Returns in the Context of Economic Uncertainty News Sentiment Using Machine Learning

Improved Complementary Pulmonary Nodule Segmentation Model Based on Multi-Feature Fusion

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