An End-to-End Deep Learning Framework for Real-Time Denoising of Heart Sounds for Cardiac Disease Detection in Unseen Noise

Ali, Shams Nafisa; Shuvo, Samiul Based; Al-Manzo, Muhammad Ishtiaque Sayeed; Hasan, Anwarul; Hasan, Taufiq

doi:10.1109/access.2023.3292551

Cited by 11 publications

(2 citation statements)

References 51 publications

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“…U-net is very popular network used in denoising, it includes the encoder and decoder that allows the network to learn important features of the signal. In papers [12][13][14], denoising autoencoders have been employed to extract meaningful information from various noisy signals, including vibrational data and electrocardiograms (ECG). Recently, in paper [15], the authors used generative adversarial network to enhance the structural signal to assess the structural damage after disasters such as earthquake.…”

Section: Introductionmentioning

confidence: 99%

Signal denoising for thermal acoustic imaging

Aggrawal,

Islam,

Zafar

et al. 2024

Photons Plus Ultrasound: Imaging and Sensing 2024

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Thermoacoustic imaging (TAI) combines microwave energy's penetration depth with ultrasound's spatial resolution for medical imaging. Denoising is crucial in TAI to refine low energy thermoacoustic signals, overcoming depth limitations and improving imaging precision. We utilized the MI-TAT system to capture signals from different phantoms and gather data for training and validation. Our architectural approach harnesses both time and spatial signal features, enabling the design of an advanced deep-learning model.

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Section: Introductionmentioning

confidence: 99%

Signal denoising for thermal acoustic imaging

Aggrawal,

Islam,

Zafar

et al. 2024

Photons Plus Ultrasound: Imaging and Sensing 2024

View full text Add to dashboard Cite

show abstract

“…In recent years, the fields of object detection [10], image segmentation [11,12], and disease recognition [13] have undergone a dramatic transformation due to the emerging advantages of machine learning (ML) and deep learning (DL) approaches [14]. Consequently, UAV detection [15] has gained popularity in the scientific community following the advent of DL techniques.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

Rahman,

Sejan,

Aziz

et al. 2024

Remote Sensing

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Autonomous unmanned aerial vehicles (UAVs) have several advantages in various fields, including disaster relief, aerial photography and videography, mapping and surveying, farming, as well as defense and public usage. However, there is a growing probability that UAVs could be misused to breach vital locations such as airports and power plants without authorization, endangering public safety. Because of this, it is critical to accurately and swiftly identify different types of UAVs to prevent their misuse and prevent security issues arising from unauthorized access. In recent years, machine learning (ML) algorithms have shown promise in automatically addressing the aforementioned concerns and providing accurate detection and classification of UAVs across a broad range. This technology is considered highly promising for UAV systems. In this survey, we describe the recent use of various UAV detection and classification technologies based on ML and deep learning (DL) algorithms. Four types of UAV detection and classification technologies based on ML are considered in this survey: radio frequency-based UAV detection, visual data (images/video)-based UAV detection, acoustic/sound-based UAV detection, and radar-based UAV detection. Additionally, this survey report explores hybrid sensor- and reinforcement learning-based UAV detection and classification using ML. Furthermore, we consider method challenges, solutions, and possible future research directions for ML-based UAV detection. Moreover, the dataset information of UAV detection and classification technologies is extensively explored. This investigation holds potential as a study for current UAV detection and classification research, particularly for ML- and DL-based UAV detection approaches.

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An Evolutionary Deep Learning for Respiratory Sounds Analysis: A Survey

Albakaa,

Alb-Salih

2024

Lecture Notes in Networks and Systems

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An End-to-End Deep Learning Framework for Real-Time Denoising of Heart Sounds for Cardiac Disease Detection in Unseen Noise

Cited by 11 publications

References 51 publications

Signal denoising for thermal acoustic imaging

Signal denoising for thermal acoustic imaging

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

An Evolutionary Deep Learning for Respiratory Sounds Analysis: A Survey

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