Kidney Stone Recognition and Extraction using Directional Emboss &amp; SVM from Computed Tomography Images

Soni, Akanksha; Rai, Avinash

doi:10.1109/mpcit51588.2020.9350388

Cited by 12 publications

(4 citation statements)

References 3 publications

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“…This section explains various DL and TL algorithms used in kidney stone segmentation and classification algorithms. Soni et al 2020 [5] proposed an automated kidney stone classification using machine learning. An efficient ML algorithm of Support Vector Machine is used to classify the images into normal and stone affected kidney images.…”

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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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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“…and color Doppler are used to diagnose kidney stone disease [12]. Te main focus of the studies is based on the information on whether the stone is found in the image or not, and it has not been represented the boundary of the stones in visual results as we performed in our study.…”

Section: Kidney Stonesmentioning

confidence: 99%

“…Te authors obtained 95% accuracy, 94% sensitivity, and 96% specifcity. Soni and Rai [12] also used CT images to detect kidney stones. Histogram equalization was used as a preprocessing step, and emboss was applied to calculate the diferences in colors according to the directions.…”

Section: Kidney Stonesmentioning

confidence: 99%

Exploring the Effect of Image Enhancement Techniques with Deep Neural Networks on Direct Urinary System X‐Ray (DUSX) Images for Automated Kidney Stone Detection

Kilic,

Karabey Aksakalli,

Tumuklu Ozyer

et al. 2023

International Journal of Intelligent Systems

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In diagnosing kidney stone disease, clinical specialists often apply medical imaging techniques such as CT and US. Among these imaging techniques, is frequently chosen as the primary examination method in emergency services due to its low cost, accessibility, and low radiation levels. However, interpreting the images by inexperienced specialists can be challenging due to the low image quality and the presence of noise. In this study, we propose a computer-aided diagnosis system based on deep neural networks to assist clinical specialists in detecting kidney stones using Direct Urinary System (DUSX) images. Firstly, in consultation with clinical specialists, we created a new dataset composed of 630 DUSX images and presented it publicly. We also defined preprocessing steps that incorporate image enhancement techniques such as GF, LoG, BF, HE, CLAHE, and CBC to enable deep neural networks to perceive the images more clearly. With these techniques, we considered the noise reduction in the DUSX images and enhanced the poor quality, especially in terms of contrast. For each preprocessing step, we created models to detect kidney stones using YOLOv4 and Mask R-CNN architectures, which are common CNN-based object detectors. We examined the effects of the preprocessing steps on these models. To the best of our knowledge, the combination of BF and CLAHE which is called CBC in this study, has not been applied before in the literature to enhance DUSX images. In addition, this study is the first in its field in which the YOLOv4 and Mask R-CNN architectures have been used for the detection of kidney stones. The experimental results demonstrated the most accurate method is the YOLOv4 model, which includes the CBC preprocessing step, as the result model. This model shows that the accuracy rate, precision, recall, and F1-score were found as 96.1%, 99.3% 96.5%, and 97.9% respectively in the test set. According to these performance metrics, we expect that the proposed model will help to reduce the unnecessary radiation exposure and associated medical costs that come with CT scans.

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“…The results of the experiments showed that the method obtained an accuracy of 96% in the classification. [15] applied a histogram equalization to the CT images used as input, followed by edge enhancement using convolutional filters. After pre-processing, the SVM (Support Vector Machine) classifier [16] was applied to differentiate kidneys with stones from those without.…”

Section: Introductionmentioning

confidence: 99%

Automatic Kidney Stone Detection using Low-cost CNN with Coronal CT Images

Bouzon,

Oliveira,

Fugita

et al. 2023

Anais Do XVIII Workshop De Visão Computacional (WVC 2023)

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A fast diagnosis of kidney stones is crucial to start the correct treatment, minimizing the risks of urinary complications. Machine learning approaches are valuable for an automatic diagnosis system for kidney stones from computer tomography (CT) exams. Recently, related studies achieved high accuracy in detecting kidney stones using deep-learning neural networks. However, their approaches were highly complex and time-consuming. This paper proposes a method for automatically detecting kidney stones on CT images using low-complexity deep-learning techniques. We compared three models based on Convolutional Neural Networks (CNN): 3-layered CNN (Conv3), 4-layered CNN (Conv4), and MobileNetV1. They were applied to kidney stone detection using 1799 CT images divided into 80% for training, 10% for validation, and 10% for testing. The proposed model Conv4 obtained the best performance, achieving a test accuracy of 97.2% and an F1-score of 97.6%, with a training time of 140 seconds.

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Kidney Stone Recognition and Extraction using Directional Emboss & SVM from Computed Tomography Images

Cited by 12 publications

References 3 publications

An Optimized Transfer Learning Model Based Kidney Stone Classification

An Optimized Transfer Learning Model Based Kidney Stone Classification

Exploring the Effect of Image Enhancement Techniques with Deep Neural Networks on Direct Urinary System X‐Ray (DUSX) Images for Automated Kidney Stone Detection

Automatic Kidney Stone Detection using Low-cost CNN with Coronal CT Images

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