Cardiac phase detection in echocardiography using convolutional neural networks

Farhad, M. M.; Masud, Mohammad Mehedy; Beg, Azam; Zaki, Nazar; Ahmed, Luai A.; Memon, Sufyan Ali

doi:10.1038/s41598-023-36047-x

Cited by 9 publications

(3 citation statements)

References 27 publications

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“…Our method demonstrates comparable or superior performance, highlighting its effectiveness across different imaging modalities. Farhad et al [47] presented cardiac phase detection in echocardiography using convolutional neural networks.…”

Section: Comparative Experiments With Existing Methodsmentioning

confidence: 99%

A Heart Image Segmentation Method Based on Position Attention Mechanism and Inverted Pyramid

Luo,

Wang,

Zou

et al. 2023

Sensors

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In the realm of modern medicine, medical imaging stands as an irreplaceable pillar for accurate diagnostics. The significance of precise segmentation in medical images cannot be overstated, especially considering the variability introduced by different practitioners. With the escalating volume of medical imaging data, the demand for automated and efficient segmentation methods has become imperative. This study introduces an innovative approach to heart image segmentation, embedding a multi-scale feature and attention mechanism within an inverted pyramid framework. Recognizing the intricacies of extracting contextual information from low-resolution medical images, our method adopts an inverted pyramid architecture. Through training with multi-scale images and integrating prediction outcomes, we enhance the network’s contextual understanding. Acknowledging the consistent patterns in the relative positions of organs, we introduce an attention module enriched with positional encoding information. This module empowers the network to capture essential positional cues, thereby elevating segmentation accuracy. Our research resides at the intersection of medical imaging and sensor technology, emphasizing the foundational role of sensors in medical image analysis. The integration of sensor-generated data showcases the symbiotic relationship between sensor technology and advanced machine learning techniques. Evaluation on two heart datasets substantiates the superior performance of our approach. Metrics such as the Dice coefficient, Jaccard coefficient, recall, and F-measure demonstrate the method’s efficacy compared to state-of-the-art techniques. In conclusion, our proposed heart image segmentation method addresses the challenges posed by diverse medical images, offering a promising solution for efficiently processing 2D/3D sensor data in contemporary medical imaging.

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Section: Comparative Experiments With Existing Methodsmentioning

confidence: 99%

A Heart Image Segmentation Method Based on Position Attention Mechanism and Inverted Pyramid

Luo,

Wang,

Zou

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The literature review of previous works on the detection and classification of myocardial infarction in echocardiogram frames has highlighted the importance Literature Review (Farhad et al, 2023) [6].The literature review of previous works on the detection and classification of myocardial infarction in echocardiogram frames has highlighted the importance of incorporating advanced deep learning techniques for improved diagnostic accuracy. (Zhu He, 2020) [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Detecting and Classifying Myocardial Infarction in Echocardiogram Frames With an Enhanced CNN Algorithm and ECV-3D Network

Deepika,

Jaisankar

2024

IEEE Access

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Myocardial infarction is a serious medical condition that requires prompt and accurate diagnosis for effective treatment. In this paper, we present a novel approach for detecting and classifying MI in echocardiogram frames using an enhanced CNN algorithm and ECV-3D network. The proposed method aims to improve the accuracy and efficiency of MI diagnosis by leveraging advanced deep learning techniques. Through extensive experimentation, we demonstrate the effectiveness of our approach in achieving high accuracy and robustness in MI detection and classification. The results indicate the potential of our method to aid in the early and precise diagnosis of MI, thereby contributing to improved patient outcomes and clinical decision-making. After conducting thorough experimentation, our proposed approach has achieved an impressive accuracy of 97.05% in the detection and classification of myocardial infarction in echocardiogram frames. This accomplishment showcases the robustness and reliability of our method, indicating its potential to significantly impact the accurate diagnosis of MI and subsequently improve patient outcomes. Furthermore, the area under the curve attained by our model is 0.82%, reaffirming the efficacy of the enhanced CNN algorithm and ECV-3D network in accurately detecting and classifying MI.It is noteworthy that all the parameters utilized in our approach have demonstrated a high level of accuracy, emphasizing the effectiveness of our deep learning techniques in enhancing the diagnostic process for MI. Moreover, the proposed method is capable of efficiently processing large volumes of echocardiogram frames, making it suitable for real-time clinical applications.

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“…Using 20 human annotators to label useful instances, the resulting model achieves similar performance to a reference model with far fewer labeled examples. This demonstrates active learning can efficiently train specialized audio classifiers for unusual sounds using minimal human annotation effort 6,7…”

Section: Introductionmentioning

confidence: 99%

An innovative deep active learning approach for improving unlabeled audio classification by selectively querying informative instance

Salama

2023

International Journal of Engineering Business Management

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Audio classification tasks like speech recognition and acoustic scene analysis require substantial labeled data, which is expensive. This work explores active learning to reduce annotation costs for a sound classification problem with rare target classes where existing datasets are insufficient. A deep convolutional recurrent neural network extracts spectro-temporal features and makes predictions. An uncertainty sampling strategy queries the most uncertain samples for manual labeling by experts and non-experts. A new alternating confidence sampling strategy and two other certainty-based strategies are proposed and evaluated. Experiments show significantly higher accuracy than passive learning baselines with the same labeling budget. Active learning generalizes well in a qualitative analysis of 20,000 unlabeled recordings. Overall, active learning with a novel sampling strategy minimizes the need for expensive labeled data in audio classification, successfully leveraging unlabeled data to improve accuracy with minimal supervision.

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Cardiac phase detection in echocardiography using convolutional neural networks

Cited by 9 publications

References 27 publications

A Heart Image Segmentation Method Based on Position Attention Mechanism and Inverted Pyramid

A Heart Image Segmentation Method Based on Position Attention Mechanism and Inverted Pyramid

Detecting and Classifying Myocardial Infarction in Echocardiogram Frames With an Enhanced CNN Algorithm and ECV-3D Network

An innovative deep active learning approach for improving unlabeled audio classification by selectively querying informative instance

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