Echocardiography image segmentation using feed forward artificial neural network (FFANN) with fuzzy multi-scale edge detection (FMED)

Shakeel, P. Mohamed; Baskar, S.; Sampath, R.; Jaber, Mustafa Musa

doi:10.1504/ijsise.2019.100651

Cited by 48 publications

(9 citation statements)

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“…We get the image at the input layer and then initialize the weights to avoid layer activation outputs from exploding or vanishing during a feed-forward propagation [30]. After weight initialization, all of the weights 'w' multiplied by input 'x' are summed up and add a bias of 1 to allow units to learn an appropriate threshold.…”

Section: A Base Modelmentioning

confidence: 99%

SIS-CNN: Semantic Image Segmentation Using Convolutional Neural Networks

Tahir

Xiao

Shaker

2021

International Journal of Advanced Network, Monitoring and Controls

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Semantic image segmentation is a vast area of interest for computer vision which has gained exceptional attention from the research community. It is the process of classifying each pixel in respective category. In this paper, we exploit the problem of scene understanding and perform the segmentation by combining different classification models as a feature encoder and segmentation models as a feature decoder. All of the experiments were performed on Camvid dataset. It covers a wide range of real-world applications such as autonomous driving, virtual/augmented reality, indoor navigation, etc.

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Section: A Base Modelmentioning

confidence: 99%

SIS-CNN: Semantic Image Segmentation Using Convolutional Neural Networks

Tahir

Xiao

Shaker

2021

International Journal of Advanced Network, Monitoring and Controls

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“…Beyond that, all parties involved in the industry, including analysts, experts, and supporters, continuously alter data to update play-by-play or make predictions [21]. Big data analytics and artificial intelligence have revolutionized the sports sector by clarifying statistical information and managing quantitative and qualitative data into understandable and stable content [22].…”

Section: Introduction To Sports Data Visualizationmentioning

confidence: 99%

Hybrid design for sports data visualization using AI and big data analytics

Liu

Mahapatra

Mayuri

2021

Complex Intell. Syst.

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In sports data analysis and visualization, understanding collective tactical behavior has become an integral part. Interactive and automatic data analysis is instrumental in making use of growing amounts of compound information. In professional team sports, gathering and analyzing sportsperson monitoring data are common practice, intending to evaluate fatigue and succeeding adaptation responses, analyze performance potential, and reduce injury and illness risk. Data visualization technology born in the era of big data analytics provides a good foundation for further developing fitness tools based on artificial intelligence (AI). Hence, this study proposed a video-based effective visualization framework (VEVF) based on artificial intelligence and big data analytics. This study uses the machine learning method to categorize the sports video by extracting both the videos' temporal and spatial features. Our system is based on convolutional neural networks united with temporal pooling layers. The experimental outcomes demonstrate that the recommended VEVF model enhances the accuracy ratio of 98.7%, recall ratio of 94.5%, F1-score ratio of 97.9%, the precision ratio of 96.7%, the error rate of 29.1%, the performance ratio of 95.2%, an efficiency ratio of 96.1% compared to other existing models.

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“…But now, with the invasion of the advanced cutting-edge technology, Internet of Medical Things (IoMT), studies are showing that the inflammation found in periodontal may play a more specific role in causing or increasing the risk for certain medical conditions [3]. IoMT has revolutionized health care sector drastically since last decade [4,5]. As shown in figure 1, IoMT is a combination of medical sensors and wearable devices that can connect to health care storage systems via networking technologies allowing the transfer of medical data over a secure network [6].…”

Section: Introductionmentioning

confidence: 99%

Predicting Neurological Disorders Linked to Oral Cavity Manifestations Using an IoMT-Based Optimized Neural Networks

et al. 2020

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Anatomically, oral cavity and central nervous system have a close relationship; the mouth and face are the location for 30-40% of the body's sensory and motor nerves. The identification of orofacial manifestations of neurological disorders is usually in direct relation with the responsibilities of a dental surgeon. Therefore, familiarizing dental surgeons with theses manifestations is essential to have better recognition, diagnosis, and correct decisions upon treating their associated Neurological Disorders. These manifestations should be efficiently analyzed using novel effective techniques since their related neurological disorders need to be early identified to avoid serious consequences. Furthermore, preventive dental care for patients with neurological disorders and all kind of rehabilitative treatments necessitates well-planned and effective novel approaches. The Internet of Medical Thing (IoMT) is a relatively new technology that has revolutionized health care sector drastically since last decade. It's a combination of medical sensors and wearable devices that can connect to health care storage systems via networking technologies allowing the transfer of medical data over a secure network. The data transferred are of utmost importance in diseases diagnosis and treatment. In this paper, an IoMT-based Intelligent Guided Particle Local Search with Optimized Neural Networks (IGPLONN) approach is proposed. Firstly, dental data are collected from the International Collaboration on Cancer Reporting (ICCR) oral cavity and central nervous system. Secondly, features are extracted from data and IGPLONN algorithm is utilized to select the effective features by minimizing the feature dimension that helps improve the overall prediction rate. Finally, the obtained features are transferred to the central health application through the IoMT platform where they can be analyzed by dental practitioners for neurological disorders prediction. The hybrid optimized technique improves the overall oral-linked neurological diseases detection rate. Moreover, it efficiently manages the forecast parameters that are used to predict the dental metastasis with minimum computational complexity. The performance of the proposed system has been experimentally evaluated on MATLAB to verify its excellence. The results revealed that proposed IoMT-based IGPLONN method attains the maximum accuracy of 98.3% compared to other methods. INDEX TERMS Oral cavity, neurological disorder, central nervous system, intelligent guided particle local search algorithm, neural network, Internet of Medical Things (IoMT).

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Echocardiography image segmentation using feed forward artificial neural network (FFANN) with fuzzy multi-scale edge detection (FMED)

Cited by 48 publications

References 0 publications

SIS-CNN: Semantic Image Segmentation Using Convolutional Neural Networks

SIS-CNN: Semantic Image Segmentation Using Convolutional Neural Networks

Hybrid design for sports data visualization using AI and big data analytics

Predicting Neurological Disorders Linked to Oral Cavity Manifestations Using an IoMT-Based Optimized Neural Networks

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