Segmentation of Coronary Arteries Images Using Spatio-temporal Feature Fusion Network with Combo Loss

Zhu, Hongyan; Song, Shaojian; Xu, Lisong; Song, Along; Yang, Benqiang

doi:10.1007/s13239-021-00588-x

Cited by 17 publications

(4 citation statements)

References 24 publications

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“…Within heart MRI, the segmentation of ventricles emerges as a promising domain for applying ML methods. This approach streamlines the measurement of ventricular volumes, contributing to heightened precision and consistency in clinical evaluations [23].Avendi et al [24]utilized deep learning algorithms trained on cardiac MRI data for the automatic classification and segmentation of the right ventricular chamber to improve algorithmic accuracy. Similarly, various automated NN has been effectively created for left ventricle classification, especially in cardiac cine MRI.…”

Section: Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

Untitled

2024

IJAIA

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The global upswing in cardiovascular disease (CVD) cases presents a critical challenge. While the ultimate goal remains elusive, improving CVD prediction accuracy is vital. Machine learning and deep learning are crucial for decoding complex health data, enhancing cardiac imaging, and predicting disease outcomes in clinical practice. This systematic literature review meticulously analyses CVD using machine learning techniques, with a particular emphasis on algorithms for classification and prediction. The metaanalysis covers 343 references from 2020 to November 2023, preceding a thorough examination of 65 selected references. Acknowledging current hurdles in CVD classification methods that impede practical use, this systematic literature review (SLR) is conducted.The study provides valuable insights for researchers and healthcare professionals, facilitating the integration of clinical applications in machine learning settings related to CVD. It also aids in promptly identifying potential threats and implementing precautionary measures. The study also recognizes prevalent classical machine learning methods, emphasizing their clinically relevant diagnostic outcomes.Deliberating on current trends, algorithms, and potential areas for future research offers a comprehensive insight into the present state of affairs.

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Section: Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

Untitled

2024

IJAIA

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“…In this study, the algorithmic level was used, where the weight for the class variables can be changed based on the number of training instances in each class. The proper way is to try a series of different weight ratios to both the class variable and measure the performance matrices [107][108][109]. The best weight ratio is the optimum solution based on the result.…”

Section: Data Imbalance and Exploratory Analysismentioning

confidence: 99%

Detection of Cardiovascular Disease from Clinical Parameters Using a One-Dimensional Convolutional Neural Network

Mamun

Elfouly

2023

Bioengineering

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Heart disease is a significant public health problem, and early detection is crucial for effective treatment and management. Conventional and noninvasive techniques are cumbersome, time-consuming, inconvenient, expensive, and unsuitable for frequent measurement or diagnosis. With the advance of artificial intelligence (AI), new invasive techniques emerging in research are detecting heart conditions using machine learning (ML) and deep learning (DL). Machine learning models have been used with the publicly available dataset from the internet about heart health; in contrast, deep learning techniques have recently been applied to analyze electrocardiograms (ECG) or similar vital data to detect heart diseases. Significant limitations of these datasets are their small size regarding the number of patients and features and the fact that many are imbalanced datasets. Furthermore, the trained models must be more reliable and accurate in medical settings. This study proposes a hybrid one-dimensional convolutional neural network (1D CNN), which uses a large dataset accumulated from online survey data and selected features using feature selection algorithms. The 1D CNN proved to show better accuracy compared to contemporary machine learning algorithms and artificial neural networks. The non-coronary heart disease (no-CHD) and CHD validation data showed an accuracy of 80.1% and 76.9%, respectively. The model was compared with an artificial neural network, random forest, AdaBoost, and a support vector machine. Overall, 1D CNN proved to show better performance in terms of accuracy, false negative rates, and false positive rates. Similar strategies were applied for four more heart conditions, and the analysis proved that using the hybrid 1D CNN produced better accuracy.

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“…To better identify and categorize images of a human's cardiac vessels, the assessment program and clinical instruments are enhanced by the proposed framework [15]. Automatic classification and segmentation of human blood vessels help cardiologists detect different heart diseases [16].…”

Section: Introductionmentioning

confidence: 99%

Efficient Hybrid Deep Learning Network Model for Segmentation and Classification of Heart Angiographic Images

Devi,

Ramaswami

2024

Nano Biomedicine and Engineering

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Heart angiography is a test that allows the treating physician to identify heart vessel abnormalities. The concerned doctor must spend a lot of time on this diagnosis. The heart vessels' targeted areas are segmented, and the blood vessel types are classified in the proposed approach. The heart angiography segmentation and classification provide essential information for the patient and the doctor. In contrast, the charge for cardiac angiography is a risky, mentally challenging task for the doctor (a heart specialist). Improved transparency and faster detection of heart diseases can result from automated segmentation and categorization of representations of heart blood vessels. Since accuracy is critical in classification, computer vision researchers are introducing several techniques; however, achieving high accuracy remains challenging when classifying heart diseases. This research proposes a multi-class ensemble classification mechanism-based computer-aided hybrid deep learning (DL) network model for localizing human blood vessels inside heart coronary computed tomography (CT) angiography images. First, the modified U-Net model is used to segment the heart blood vessels, and the self-attention network (SAT-Net) model is used to extract various features from the segmented blood vessel images. In segmented human heart blood vessels, low-level features are retrieved, including geometrical, statistical, and texture features. The enhanced elman spike neural network (EESNN) model has been used to finally classify cardiac blood vessels into four distinct groups: blood flow-reduced, narrow, block, and regular vessels. The suggested strategy has produced the best results, creating a conducive environment for cardiologists to diagnose heart-related disorders that is quick, simple, accurate, and time-saving.

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Segmentation of Coronary Arteries Images Using Spatio-temporal Feature Fusion Network with Combo Loss

Cited by 17 publications

References 24 publications

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Untitled

Detection of Cardiovascular Disease from Clinical Parameters Using a One-Dimensional Convolutional Neural Network

Efficient Hybrid Deep Learning Network Model for Segmentation and Classification of Heart Angiographic Images

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