Deep learning computer vision algorithm for detecting kidney stone composition

Black, Kristian M; Law, Hei; Aldoukhi, Ali H.; Deng, Jia; Ghani, Khurshid R.

doi:10.1111/bju.15035

Cited by 124 publications

(94 citation statements)

References 9 publications

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“…To determine the type of stones, the type of kidney stone was classified from endoscopic video images with a deep learning network trained with digital photographs of five types of kidney stone components. This classification aims to automatically determine the laser energy settings manually adjusted according to the kidney stone component and size [9] use a convolutional neural network to classify kidney stone type. In addition, the positions, shapes and sizes of kidney stones are different from each other.…”

Section: Related Workmentioning

confidence: 99%

Kidney X-ray Images Classification using Machine Learning and Deep Learning Methods

Aksakalli

Kaçdioğlu

Hanay

2021

Balkan Journal of Electrical and Computer Engineering

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Today, kidney stone detection is performed manually by humans on medical images. This process is timeconsuming and subjective as it depends on the physician. This study aims to classify healthy or patient individuals according to the status of kidney stones from medical images using various machine learning methods and Convolutional Neural Network (CNN). We evaluated various machine learning methods such as Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVM), Multilayer Perceptron (MLP), K-Nearest Neighbor (kNN), Naive Bayes (BernoulliNB), and deep neural networks using CNN. According to the experiments, the Decision Tree Classifier (DT) has the best classification result. This method has the highest F1 score rate with a success rate of 85.3% using the S+U sampling method. The experimental results show that the Decision Tree Classifier (DT) is a feasible method for distinguishing the kidney x-ray images.

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Section: Related Workmentioning

confidence: 99%

Kidney X-ray Images Classification using Machine Learning and Deep Learning Methods

Aksakalli

Kaçdioğlu

Hanay

2021

Balkan Journal of Electrical and Computer Engineering

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“…Multi-Class Classification Model is used in deep CNN to each image. The overall weighted REC of the CNNs composition analysis was 85% [33].…”

Section: Literature Reviewmentioning

confidence: 99%

Role of Machine Vision for Identification of Kidney Stones Using Multi Features Analysis

Qadri¹

2021

Research Journal Of Computer Science And Information Technology

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The purpose of this study is to highlight the significance of machine vision for the Classification of kidney stone identification. A novel optimized fused texture features frame work was designed to identify the stones in kidney. A fused 234 texture feature namely (GLCM, RLM and Histogram) feature set was acquired by each region of interest (ROI). It was observed that on each image 8 ROI’s of sizes (16x16, 20x20 and 22x22) were taken. It was difficult to handle a large feature space 280800 (1200x234). Now to overcome this data handling issue we have applied feature optimization technique namely POE+ACC and acquired 30 most optimized features set for each ROI. The optimized fused features data set 3600(1200x30) was used to four machine vision Classifiers that is Random Forest, MLP, j48 and Naïve Bayes. Finally, it was observed that Random Forest provides best results of 90% accuracy on ROI 22x22 among the above discussed deployed Classifiers

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“…Recently, deep learning techniques have been used to solve many computer vision tasks [14,[16][17][18][19][20]. In particular, CNNs are good image classifiers [21][22][23][24][25][26].…”

Section: Related Workmentioning

confidence: 99%

Environment Classification for Unmanned Aerial Vehicle Using Convolutional Neural Networks

et al. 2020

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Environment classification is one of the most critical tasks for Unmanned Aerial Vehicles (UAV). Since water accumulation may destabilize UAV, clouds must be detected and avoided. In a previous work presented by the authors, Superpixel Segmentation (SPS) descriptors with low computational cost are used to classify ground, sky, and clouds. In this paper, an enhanced approach to classify the environment in those three classes is presented. The proposed scheme consists of a Convolutional Neural Network (CNN) trained with a dataset generated by both, an human expert and a Support Vector Machine (SVM) to capture context and precise localization. The advantage of using this approach is that the CNN classifies each pixel, instead of a cluster like in SPS, which improves the resolution of the classification, also, is less tedious for the human expert to generate a few training samples instead of the normal amount that it is required. This proposal is implemented for images obtained from video and photographic cameras mounted on a UAV facing in the same direction of the vehicle flight. Experimental results and comparison with other approaches are shown to demonstrate the effectiveness of the algorithm.

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Deep learning computer vision algorithm for detecting kidney stone composition

Cited by 124 publications

References 9 publications

Kidney X-ray Images Classification using Machine Learning and Deep Learning Methods

Kidney X-ray Images Classification using Machine Learning and Deep Learning Methods

Role of Machine Vision for Identification of Kidney Stones Using Multi Features Analysis

Environment Classification for Unmanned Aerial Vehicle Using Convolutional Neural Networks

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