Detection of pulmonary hypertension with six training strategies based on deep learning technology

Wang, Miao; Wang, JiWen; Hu, Yating; Guo, Binbin; Tang, Hong

doi:10.1111/coin.12527

Cited by 2 publications

(1 citation statement)

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“…Here, Laplace Gaussian mutation‐based moth flame optimization and the grasshopper optimization technique were used to optimise the tuning parameters of the proposed TANFIS. In order to detect the PH using the HS signals, Wang et al 30 constructed and tested a wavelet scattering convolution network (WSCN) based on an LSTM technique. The network was validated using 131 patients through five cross‐validations.…”

Section: Related Workmentioning

confidence: 99%

A deep ensemble network model for classifying and predicting breast cancer

Subramanian

Venugopal

2022

Computational Intelligence

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Breast cancer is one of the leading causes of death among women worldwide. In most cases, the misinterpretation of medical diagnosis plays a vital role in increased fatality rates due to breast cancer. Breast cancer can be diagnosed by classifying tumors. There are two different types of tumors, such as malignant and benign tumors. Identifying the type of tumor is a tedious task, even for experts. Hence, an automated diagnosis is necessary. The role of machine learning in medical diagnosis is eminent as it provides more accurate results in classifying and predicting diseases. In this paper, we propose a deep ensemble network (DEN) method for classifying and predicting breast cancer. This method uses a stacked convolutional neural network, artificial neural network and recurrent neural network as the base classifiers in the ensemble. The random forest algorithm is used as the meta‐learner for providing the final prediction. Experimental results show that the proposed DEN technique outperforms all the existing approaches in terms of accuracy, sensitivity, specificity, F‐score and area under the curve (AUC) measures. The analysis of variance test proves that the proposed DEN model is statistically more significant than the other existing classification models; thus, the proposed approach may aid in the early detection and diagnosis of breast cancer in women, hence aiding in the development of early treatment techniques to increase survival rate.

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Section: Related Workmentioning

confidence: 99%

A deep ensemble network model for classifying and predicting breast cancer

Subramanian

Venugopal

2022

Computational Intelligence

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Deep learning technology in vascular image segmentation and disease diagnosis

Du,

Zhuang,

Huang

2024

Journal of Intelligent Medicine

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Blood vessel segmentation is a crucial aspect of medical image processing, aiding medical professionals in more accurate disease analysis and diagnosis. Manual blood vessel segmentation methods are time‐consuming and cumbersome, making the development of automatic segmentation methods essential. The rapid advancements in deep learning technology have introduced new tools and methods for vascular image segmentation. In this review, we provide a comprehensive overview of deep learning‐based blood vessel segmentation methods across various fields, including retinal vessel segmentation, cerebrovascular segmentation, and pulmonary vessel segmentation. Several prevalent diseases, such as retinal vascular diseases, cerebrovascular diseases, pulmonary vascular diseases, and tumors, have posed significant health challenges globally. This review also discusses the application of deep learning technology in disease diagnosis within these contexts. Finally, considering the current research landscape, we discuss existing challenges and potential future developments in blood vessel segmentation. We aim to assist researchers in gaining a comprehensive understanding and designing effective blood vessel segmentation models, ultimately offering opportunities for early disease diagnosis and treatment.

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Detection of pulmonary hypertension with six training strategies based on deep learning technology

Abstract: Pulmonary hypertension (PH) is a progressive condition with high mortality. At present, the most accurate diagnosis method is invasive. However, noninvasive methods existed are inaccurate, and could not be used for continuous monitoring. The heart sound (HS) sig-

Cited by 2 publications

References 51 publications

A deep ensemble network model for classifying and predicting breast cancer

A deep ensemble network model for classifying and predicting breast cancer

Deep learning technology in vascular image segmentation and disease diagnosis

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