VLSI Implementation and Analysis of Kidney Stone Detection from Ultrasound Image by Level Set Segmentation and MLP-BP ANN Classification

Viswanath, K.; Gunasundari, R.

doi:10.1007/978-81-322-2656-7_19

Cited by 6 publications

(5 citation statements)

References 16 publications

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“…The design is highly protected and the less complex system comprises, the cloud architecture, system handling for complexity and management of cloud access control. This system is performed for multiple users with various cloud models with high security and recovery of the data, segmentation of data is discussed in [32]. As depicted in Fig.…”

Section: B Affine Coordinatesmentioning

confidence: 99%

Performance Analysis of Cloud-based Health Care Data Privacy System Using Hybrid Techniques

Kumari¹,

Asha²,

Rajashekhar³

et al. 2022

International Journal of Biology and Biomedical Engineering

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At present, due to the various hacking approaches, the protection for any data transmitted through any channel or mode is one of the important issues. Nowadays, providing data security is satisfactory, developments are extended for obtaining data among the transceivers. Security level depends on the size of a symmetric key which is employed for encryption and decryption using various cryptography systems management and in modern approaches like block and RF codes including AES use a larger size of key simultaneously and there exists security problems due to hacking approaches. To illustrate the protection level and hacking problems, a new ECC is presented as well as by employing scalar duplication, the synchronous key is generated and consists of point doubling and point addition. The created focuses are encrypted before transmission by using ECC-Elgamal-Holomorphic (ECCEH) and transferred through a distant channel and encipher data is failed at the receiver using ECCEH which includes the reverse process. The unique standards of cryptography context have been generated by MATLAB; the defined framework has endeavored to the extent that speed, delay as well as control, and many others are accepted in MATLAB 2017a. The user of the sender, the original information is transformed into integer value by employing Holomorphic and encodes it by utilizing the Elgamal ECC algorithm which employs point doubling and point addition. The encoded information is uploaded into the cloud for storage, here www.thingspeak.com is utilized for storage. When the user presents at the receiver request the cloud to access from it, initially the cloud server authenticates the access control strategies of the requester, and then access is provided by the cloud server. If the user authenticates the strategies, then encoded data can download and the original data is decoded by synchronous key employing ECC- Elgamal algorithm. Using original and decrypted data, various performance factors are calculated in terms of execution time, packet delivery ratio, throughput, latency and compare these results with conventional methods and found to be 12%, 31%, 24%, and 8% progress concerned with packet delivery ratio, latency, outturn and execution time.

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Section: B Affine Coordinatesmentioning

confidence: 99%

Performance Analysis of Cloud-based Health Care Data Privacy System Using Hybrid Techniques

Kumari¹,

Asha²,

Rajashekhar³

et al. 2022

International Journal of Biology and Biomedical Engineering

Self Cite

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“…3. The architecture of the neural network [20] Алгоритм в основном классифицировал изображение с камнями и без с помощью классификатора, а затем изображения с камнями подвергались дополнительному сегментированию для определения локализации камней. Исследование показало, что кластеризация «multi kernel» k-средних (гибридная линейная и квадратичная модель) достигла точности 99,6% [19].…”

Section: определение локализации конкремента по данным узи и ктunclassified

“…Для детекции камней Viswanath и соавт. использовали ансамбль архитектур -многослойный перцептрон Румельхарта [20]. Обучение нейросети проводилось на ретроспективной базе данных 500 пациентов с предварительной подготовкой УЗИ изображений с использованием набора фильтров.…”

Section: определение локализации конкремента по данным узи и ктunclassified

Artificial intelligence in the diagnosis and treatment of kidney stone disease.

Пранович¹,

Ismailov²,

Karelskaya³

et al. 2022

RJTEH

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Complex health-care data will be growing exponentially as we enter the era of “big data”. Interpretation and analysis of a large amount of medical data for rapid and personalized decision-making require artificial intelligence-driven technologies and data mining techniques. Over the past decade, a rapid transition to the analysis, treatment and monitoring of kidney stone disease using the artificial intelligence (AI) with machine learning algorithms and artificial neural networks ensures the development of precise support systems. Machine learning algorithms able to predict the location of stones using computed tomography scans or ultrasound images, determine their chemical composition, recurrence rate based on demographic and genetic variables, spontaneous ureteral calculous passage, and treatment outcomes. Recent studies use rather simple machine learning methods and small training sets that reduce their accuracy, sensitivity, and specificity and limit their routine application. Therefore, the development of complex architectures, using multilayer artificial neural networks and convolutional neural networks, and integration other heterogeneous variables will improve the resultant predictive accuracy ensuring their introduction in real clinical practice.

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“…Artificial neural networks (ANNs) are an important area of artificial intelligence (AI) used to perform several tasks, such as classification [1][2][3][4], pattern recognition [5][6][7][8], communications [9,10], control systems [11,12], prediction [13,14], among others. An ANN models a biological neural network employing a collection of nodes called artificial neurons, connected by edges to transmit signals like the synapses in a brain; during its transmission, the signal value changes according to the weight of the edges, adjusted by a learning process.…”

Section: Introductionmentioning

confidence: 99%

“…An attractive solution is the development of hardware neuronal networks (HNN) in Field-Programmable Gate Arrays (FPGAs) [15][16][17][18][19][20][21]. In this regard, the FPGA-based implementation of AFs in HNN is one of the challenges for embedded system design according to recent studies; this is because the AF implementations require low hardware resources and low power consumption [1,2,5,12,[22][23][24][25]. Currently, the most common non-linear functions for ANNs are the Sigmoid [11,[26][27][28][29][30][31][32] ans TanhAFs [22,32,33].…”

Section: Introductionmentioning

confidence: 99%

Hardware-Based Activation Function-Core for Neural Network Implementations

et al. 2021

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Today, embedded systems (ES) tend towards miniaturization and the carrying out of complex tasks in applications such as the Internet of Things, medical systems, telecommunications, among others. Currently, ES structures based on artificial intelligence using hardware neural networks (HNNs) are becoming more common. In the design of HNN, the activation function (AF) requires special attention due to its impact on the HNN performance. Therefore, implementing activation functions (AFs) with good performance, low power consumption, and reduced hardware resources is critical for HNNs. In light of this, this paper presents a hardware-based activation function-core (AFC) to implement an HNN. In addition, this work shows a design framework for the AFC that applies a piecewise polynomial approximation (PPA) technique. The designed AFC has a reconfigurable architecture with a wordlength-efficient decoder, i.e., reduced hardware resources are used to satisfy the desired accuracy. Experimental results show a better performance of the proposed AFC in terms of hardware resources and power consumption when it is compared with state of the art implementations. Finally, two case studies were implemented to corroborate the AFC performance in widely used ANN applications.

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VLSI Implementation and Analysis of Kidney Stone Detection from Ultrasound Image by Level Set Segmentation and MLP-BP ANN Classification

Cited by 6 publications

References 16 publications

Performance Analysis of Cloud-based Health Care Data Privacy System Using Hybrid Techniques

Performance Analysis of Cloud-based Health Care Data Privacy System Using Hybrid Techniques

Artificial intelligence in the diagnosis and treatment of kidney stone disease.

Hardware-Based Activation Function-Core for Neural Network Implementations

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