Machine Learning Approaches for Automated Stroke Detection, Segmentation, and Classification in Microwave Brain Imaging Systems

Roohi, Majid; Mazloum, Jalil; Pourmina, Mohammad Ali; Ghalamkari, and Behbod

doi:10.2528/pierc21080404

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…e disadvantage of iterative threshold method is that it is more sensitive to the selection of iterative initial value and threshold and cannot guarantee that the obtained solution is sparse [21][22][23][24][25].…”

Section: Related Workmentioning

confidence: 99%

Research on the Development of Localized Music Curriculum System Based on the Theory of Multiple Intelligences

Gong

2022

Scientific Programming

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The integration and development of music curriculum signals have attracted the attention of researchers in real teaching scenarios. Based on the theory of multiple intelligences algorithm, this paper constructs a music curriculum integration and development model. This paper establishes a nonnegative matrix decomposition scheme, adds a constraint term that can reflect the smoothness of the time-varying gain matrix to the divergence-based objective function, and iteratively solves the problem of curriculum integration and development through the minimum optimization algorithm obtained by constructing the auxiliary function. In the process of simulation experiment, the optimal solution of each factor matrix was designed, the source music signal was reconstructed, and the sufficiently sparse source music signal was separated. The experimental results show that, compared with the parameter estimation without preprocessing, the parameter estimation with preprocessing is more accurate, the accuracy of the improved algorithm reaches 97.1%, the signal compression ratio reaches 0.656, and the enhanced signal can obtain at most 6 dB. The signal-to-noise ratio is improved, and the convergence speed is fast and asymptotically reaches the lower bound. It is suitable for parameter estimation of low-order and high-order autoregressive processes and effectively promotes the smooth development of music course signals.

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Section: Related Workmentioning

confidence: 99%

Research on the Development of Localized Music Curriculum System Based on the Theory of Multiple Intelligences

Gong

2022

Scientific Programming

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“…Roohi et al 142 proposed an MWI-based method for detecting and classifying intracranial hemorrhage strokes using ML techniques. Sixteen modified bow-tie antenna elements are arranged in a circle around a multilayer head phantom serving as an intracranial hemorrhage target.…”

Section: State-of-art Techniques Of Ai-assisted Mwi In Disease Diagnosismentioning

confidence: 99%

Emerging paradigms in microwave imaging technology for biomedical applications: unleashing the power of artificial intelligence

Khalid,

Zubair,

Mehmood

et al. 2024

npj Imaging

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In recent years, microwave imaging (MWI) has emerged as a non-ionizing and cost-effective modality in healthcare, specifically within medical imaging. Concurrently, advances in artificial intelligence (AI) have significantly augmented the capabilities of medical imaging tools. This paper explores the intersection of these two domains, focusing on the integration of AI algorithms into MWI techniques to elevate accuracy and overall performance. Within the scope of existing literature, representative prior works are compared concerning the application of AI in both the “MWI for Healthcare Applications" and “Artificial Intelligence Assistance In MWI" sections. This comparative analysis sheds light on the diverse approaches employed to enhance the synergy between AI and MWI. While highlighting the state-of-the-art technology in MWI and its historical context, this paper delves into the historical taxonomy of AI-assisted MWI, elucidating the evolution of intelligent systems within this domain. Moreover, it critically examines prominent works, providing a nuanced understanding of the advancements and challenges encountered. Addressing the limitations and challenges inherent in developing AI-assisted MWI systems like Generalization to different conditions, Generalization to different conditions, etc the paper offers a brief synopsis of these obstacles, emphasizing the importance of overcoming them for robust and reliable results in actual clinical environments. Finally, the paper not only underscores the current advancements but also anticipates future innovations and developments in utilizing AI for MWI applications in healthcare.

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“…rough this step, the initial recommendation information pool can be obtained. In recommender systems, there are two criteria for accuracy evaluation: one is classification accuracy, and the other is prediction accuracy [22][23][24][25].…”

Section: Scientific Programmingmentioning

confidence: 99%

A Music Curriculum Integration and Reconstruction Model Based on Advanced Iterative Reconstruction Algorithm

Zeng

2022

Scientific Programming

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Music curriculum fusion and super-resolution reconstruction based on musical elements have gradually attracted the attention of researchers. The traditional music element fusion and reconstruction algorithm is based on the fusion and reconstruction of all the pixel information of the source music element, which has the problem of high time and space complexity. Based on the advanced iterative reconstruction theory, this paper uses the measurement matrix to measure the dimensionality of the music signal, compresses the music element data while acquiring the music elements, reduces the sampling frequency, reduces the sampling amount of the music element data, and greatly reduces the data. It solves the problem of spatial resolution reduction caused by degradation and the problem of music element feature extraction, classification, and identification and can obtain more music element features and detailed parameters. Aiming at the back-projection algorithm of musical symbol filtering, the advanced iterative reconstruction algorithm of musical symbol filtering of a triangular line array is simulated. The experimental results show that the feasibility analysis factor of the scheme reaches 0.917, and the running time of the reconstruction algorithm is reduced to 0.131 s, which promotes a large amount of data in music curriculum element fusion and super-resolution reconstruction.

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Machine Learning Approaches for Automated Stroke Detection, Segmentation, and Classification in Microwave Brain Imaging Systems

Cited by 7 publications

References 22 publications

Research on the Development of Localized Music Curriculum System Based on the Theory of Multiple Intelligences

Research on the Development of Localized Music Curriculum System Based on the Theory of Multiple Intelligences

Emerging paradigms in microwave imaging technology for biomedical applications: unleashing the power of artificial intelligence

A Music Curriculum Integration and Reconstruction Model Based on Advanced Iterative Reconstruction Algorithm

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