Analysis and Implementation of Kidney Stone Detection by Reaction Diffusion Level Set Segmentation Using Xilinx System Generator on FPGA

Viswanath, K.; Gunasundari, R.

doi:10.1155/2015/581961

Cited by 26 publications

(4 citation statements)

References 17 publications

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“…The FCM [10] used in this study to offer a segmentation method. Kidney Stone Detection is discussed and Artificial intelligence systems based on neural networks have shown excellent outcomes when using image processing and neural networks.…”

Section: Restricted Boltzmann Machine (Rbm) a Deep Beliefs Network Is...mentioning

confidence: 99%

Identification of Kidney Related Disease Using Deep Learning

Selvaraj¹,

Thangarajan²,

Ramya

et al. 2023

ACE

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Nowadays, kidney stone problems are frequent, as more people are suffering from acute discomfort caused by kidney stones. The main cause of this disease is a high level of unhealthy food consumption and a low level of water consumption. Most health problems can be avoided if we drink enough water, but in this fast-paced world, people often forget to do so. As a result, stone concerns have arisen and been discovered as stone sizes have grown greater. They employ coronal computed tomography (CT) scan results and other study data on the belly and thorax to determine the sizes of stones and detect them in hospitals. This could assist in locating the issues. They currently utilize a back propagation method to assist them spot problems with an accuracy of 85 percent. We suggested deep learning methods and approaches, which process training data and data sets with labels, to find a more accurate level than the previous one. To obtain an accurate result rate, a larger number of photos are compared and analyzed with the scanned images. The accuracy level has been raised to 97.6%. So that we can acquire reliable results and diagnose problems sooner. We can observe how deep learning works and how it handles urological difficulties in this paper.

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Section: Restricted Boltzmann Machine (Rbm) a Deep Beliefs Network Is...mentioning

confidence: 99%

Identification of Kidney Related Disease Using Deep Learning

Selvaraj¹,

Thangarajan²,

Ramya

et al. 2023

ACE

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“…The suggested approach achieved an F1-score of 82.92%. Viswanath et al [46] used ultrasound images to create a model to identify kidney stones, and they initially used a diffusion method to locate the kidney. Multi-layer perception (MLP) was utilized as a classifier for the characteristics retrieved from ROI.…”

Section: Machine-learning Practicesmentioning

confidence: 99%

Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAP

et al. 2023

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Kidney abnormality is one of the major concerns in modern society, and it affects millions of people around the world. To diagnose different abnormalities in human kidneys, a narrow-beam x-ray imaging procedure, computed tomography, is used, which creates cross-sectional slices of the kidneys. Several deep-learning models have been successfully applied to computer tomography images for classification and segmentation purposes. However, it has been difficult for clinicians to interpret the model’s specific decisions and, thus, creating a “black box” system. Additionally, it has been difficult to integrate complex deep-learning models for internet-of-medical-things devices due to demanding training parameters and memory-resource cost. To overcome these issues, this study proposed (1) a lightweight customized convolutional neural network to detect kidney cysts, stones, and tumors and (2) understandable AI Shapely values based on the Shapley additive explanation and predictive results based on the local interpretable model-agnostic explanations to illustrate the deep-learning model. The proposed CNN model performed better than other state-of-the-art methods and obtained an accuracy of 99.52 ± 0.84% for K = 10-fold of stratified sampling. With improved results and better interpretive power, the proposed work provides clinicians with conclusive and understandable results.

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“…Viswanath et al 2015 [6] proposed a level set segmentation-based kidney stone detection and classification method. Compared to other imaging systems, ultrasound images are easily vulnerable to speckle noises.…”

Section: Related Workmentioning

confidence: 99%

An Optimized Transfer Learning Model Based Kidney Stone Classification

Mahalakshmi¹

2023

Computer Systems Science and Engineering

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The kidney is an important organ of humans to purify the blood. The healthy function of the kidney is always essential to balance the salt, potassium and pH levels in the blood. Recently, the failure of kidneys happens easily to human beings due to their lifestyle, eating habits and diabetes diseases. Early prediction of kidney stones is compulsory for timely treatment. Image processingbased diagnosis approaches provide a greater success rate than other detection approaches. In this work, proposed a kidney stone classification method based on optimized Transfer Learning(TL). The Deep Convolutional Neural Network (DCNN) models of DenseNet169, MobileNetv2 and GoogleNet applied for classification. The combined classification results are processed by ensemble learning to increase classification performance. The hyperparameters of the DCNN model are adjusted by the metaheuristic algorithm of Gorilla Troops Optimizer (GTO). The proposed TL model outperforms in terms of all the parameters compared to other DCNN models.

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Analysis and Implementation of Kidney Stone Detection by Reaction Diffusion Level Set Segmentation Using Xilinx System Generator on FPGA

Cited by 26 publications

References 17 publications

Identification of Kidney Related Disease Using Deep Learning

Identification of Kidney Related Disease Using Deep Learning

Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAP

An Optimized Transfer Learning Model Based Kidney Stone Classification

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