Novel approach for automatic mid‐diastole frame detection in 2D echocardiography sequences for performing planimetry of the mitral valve orifice

Faraji, Mahtab; Behnam, Hamid; Cherloo, Mohammad Norizadeh; Shojaeifard, Maryam

doi:10.1049/iet-ipr.2019.1757

Cited by 4 publications

(5 citation statements)

References 36 publications

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“…To assess the ideal performance of the suggested technique, we simulated existing algorithms utilized in recent research to automatically identify driver fatigue using the proposed multi-level collected database and compared them to our own proposed algorithm. In this respect, two feature learning approaches based on raw signal and manual feature extraction were utilized, as well as standard and popular algorithms and classifications such as MLP [36], SVM [37], CNN [38] with Relu activation function [39], CNN with Leaky Relu activation function [40], Deep Boltzmann Machines (DBMs) [41], and the suggested model for the five-level scenario. The mean, peak coefficient, skewness, variance, maximum, minimum, and kurtosis were retrieved from the recorded EEG signals for engineering characteristics.…”

Section: Using Recent Methods and Algorithms To Compare With The Prop...mentioning

confidence: 99%

Deep Learning for Detecting Multi-Level Driver Fatigue Using Physiological Signals: A Comprehensive Approach

Peivandi,

Ardabili,

Sheykhivand

et al. 2023

Sensors

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A large share of traffic accidents is related to driver fatigue. In recent years, many studies have been organized in order to diagnose and warn drivers. In this research, a new approach was presented in order to detect multi-level driver fatigue. A multi-level driver tiredness diagnostic database based on physiological signals including ECG, EEG, EMG, and respiratory effort was developed for this aim. The EEG signal was used for processing and other recorded signals were used to confirm the driver’s fatigue so that fatigue was not confirmed based on self-report questionnaires. A customized architecture based on adversarial generative networks and convolutional neural networks (end-to-end) was utilized to select/extract features and classify different levels of fatigue. In the customized architecture, with the objective of eliminating uncertainty, type 2 fuzzy sets were used instead of activation functions such as Relu and Leaky Relu, and the performance of each was investigated. The final accuracy obtained in the three scenarios considered, two-level, three-level, and five-level, were 96.8%, 95.1%, and 89.1%, respectively. Given the suggested model’s optimal performance, which can identify five various levels of driver fatigue with high accuracy, it can be employed in practical applications of driver fatigue to warn drivers.

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Section: Using Recent Methods and Algorithms To Compare With The Prop...mentioning

confidence: 99%

Deep Learning for Detecting Multi-Level Driver Fatigue Using Physiological Signals: A Comprehensive Approach

Peivandi,

Ardabili,

Sheykhivand

et al. 2023

Sensors

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“…The ACI technique includes creating small biopsy of the hyaline cartilage, extracting chondrocytes from a less-weight-bearing area of articular surface, and culturing the cells in vitro [63]. Then, chondrocyte cells are expanded in vitro to enhance the number of cells to provide enough number to fill a focal articular defect [64]. Once the chondrocyte cell population achieved a certain level in vitro, they are implanted into the cartilage defect.…”

Section: Autologous Chondrocyte Implantation (Aci)mentioning

confidence: 99%

“…Different materials have been suggested for cartilage regeneration purposes (synthetic or natural) and they can be used in various physical forms of fibers, meshes, and hydrogels. Hyaluronan and collagen-based scaffolds are polymers widely used for the fabrication of cartilage scaffolds due to their similarity to the natural cartilage tissue [49,64]. Scaffolds are designed to be chondro-conductive and they can be used with or without cells.…”

Section: Autologous Chondrocyte Implantation (Aci)mentioning

confidence: 99%

“…There have been several types of research to expand and improve the hydrogel that induces chondrogenesis; however, researchers are looking for physical cues that can promote cartilage regeneration after hydrogel is added [64]. The electromagnetic hydrogels are the new generation of hydrogel for cartilage tissue generation that is shown to be successful in in vitro and in vivo studies.…”

Section: Hydrogel For Cartilage Tissue Regenerationmentioning

confidence: 99%

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Magnetic Hydrogel for Cartilage Tissue Regeneration as well as a Review on Advantages and Disadvantages of Different Cartilage Repair Strategies

Babaniamansour

Salimi

Dorkoosh

et al. 2022

BioMed Research International

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There is a clear clinical need for efficient cartilage healing strategies for treating cartilage defects which burdens millions of patients physically and financially. Different strategies including microfracture technique, osteochondral transfer, and scaffold-based treatments have been suggested for curing cartilage injuries. Although some improvements have been achieved in several facets, current treatments are still less than satisfactory. Recently, different hydrogel-based biomaterials have been suggested as a therapeutic candidate for cartilage tissue regeneration due to their biocompatibility, high water content, and tunability. Specifically, magnetic hydrogels are becoming more attractive due to their smart response to magnetic fields remotely. We seek to outline the context-specific regenerative potential of magnetic hydrogels for cartilage tissue repair. In this review, first, we explained conventional techniques for cartilage repair and then compared them with new scaffold-based approaches. We illustrated various hydrogels used for cartilage regeneration by highlighting the magnetic hydrogels. Also, we gathered in vitro and in vivo studies of how magnetic hydrogels promote chondrogenesis as well as studied the biological mechanism which is responsible for cartilage repair due to the application of magnetic hydrogel.

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“…When combined with human expertise, these systems can match the reliability of double human reading [15][16][17][18][19]. With the advent of artificial intelligence (AI), especially deep learning (DL) methods like convolutional neural networks (CNNs), the capability of CAD systems has been significantly enhanced [20][21][22][23][24][25][26][27][28]. Building on the transformative potential of AI in medical diagnostics, Bagheri et al [29] exploited AI and ML techniques to tackle diagnostic challenges in chronic limb-threatening ischemia (CLTI), highlighting the utility of such methods for precise diagnoses, outcome predictions, and identifying treatment disparities.…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Detection with an Ensemble of Deep Learning Networks Using a Consensus-Adaptive Weighting Method

Dehghan Rouzi,

Moshiri,

Khoshnevisan

et al. 2023

J. Imaging

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Breast cancer’s high mortality rate is often linked to late diagnosis, with mammograms as key but sometimes limited tools in early detection. To enhance diagnostic accuracy and speed, this study introduces a novel computer-aided detection (CAD) ensemble system. This system incorporates advanced deep learning networks—EfficientNet, Xception, MobileNetV2, InceptionV3, and Resnet50—integrated via our innovative consensus-adaptive weighting (CAW) method. This method permits the dynamic adjustment of multiple deep networks, bolstering the system’s detection capabilities. Our approach also addresses a major challenge in pixel-level data annotation of faster R-CNNs, highlighted in a prominent previous study. Evaluations on various datasets, including the cropped DDSM (Digital Database for Screening Mammography), DDSM, and INbreast, demonstrated the system’s superior performance. In particular, our CAD system showed marked improvement on the cropped DDSM dataset, enhancing detection rates by approximately 1.59% and achieving an accuracy of 95.48%. This innovative system represents a significant advancement in early breast cancer detection, offering the potential for more precise and timely diagnosis, ultimately fostering improved patient outcomes.

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Novel approach for automatic mid‐diastole frame detection in 2D echocardiography sequences for performing planimetry of the mitral valve orifice

Cited by 4 publications

References 36 publications

Deep Learning for Detecting Multi-Level Driver Fatigue Using Physiological Signals: A Comprehensive Approach

Deep Learning for Detecting Multi-Level Driver Fatigue Using Physiological Signals: A Comprehensive Approach

Magnetic Hydrogel for Cartilage Tissue Regeneration as well as a Review on Advantages and Disadvantages of Different Cartilage Repair Strategies

Breast Cancer Detection with an Ensemble of Deep Learning Networks Using a Consensus-Adaptive Weighting Method

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