Classification of Ultrasound Kidney Images using PCA and Neural Networks
TLDR
A computer-aided system is proposed for automatic classification of Ultrasound Kidney diseases and a correct classification rate of 97% has been obtained using the multi-scale wavelet-based features.Abstract:
In this paper, a computer-aided system is proposed for automatic classification of Ultrasound Kidney diseases. Images of five classes: Normal, Cyst, Stone, Tumor and Failure were considered. A set of statistical features and another set of multi-scale wavelet-based features were extracted from the region of interest (ROI) of each image and the principal component analysis was performed to reduce the number of features. The selected features were utilized in the design and training of a neural network classifier. A correct classification rate of 97% has been obtained using the multi-scale wavelet-based features.read more
Citations
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Performance Evaluation of a Proposed Machine Learning Model for Chronic Disease Datasets Using an Integrated Attribute Evaluator and an Improved Decision Tree Classifier
Sushruta Mishra,Pradeep Kumar Mallick,Hrudaya Kumar Tripathy,Akash Kumar Bhoi,Alfonso González-Briones +4 more
TL;DR: A new hybrid Attribute Evaluator method has been proposed which effectively integrates enhanced K-Means clustering with the CFS filter method and the BFS wrapper method and was evaluated with an improved decision tree classifier.
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Feature Extraction and Selection of kidney Ultrasound Images Using GLCM and PCA
Priyanka,Dharmender Kumar +1 more
TL;DR: The results show that GLCM in combination with PCA for feature reduction gives high classification accuracy when classifying images using Artificial Neural Network (ANN).
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Computer-aided diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data by integrating texture image features and deep transfer learning image features.
TL;DR: The experiments have demonstrated that the classifiers built on the transfer learning features and conventional image features could distinguish abnormal kidney images from controls with AUCs greater than 0.88, indicating that classification of ultrasound kidney scans has a great potential to aid kidney disease diagnosis.
Journal ArticleDOI
Automated detection of kidney abnormalities using multi-feature fusion convolutional neural networks
TL;DR: An automated architecture to detect various kidney abnormalities is proposed that works on abdominal ultrasound images using convolutional neural networks and is combined with a weighted ensemble method to improve performance.
Proceedings ArticleDOI
Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data
TL;DR: Experimental results have demonstrated that the combination of transfer learning features and conventional imaging features yielded the best classification performance for distinguishing CAKUT patients from normal controls based on their US kidney images.
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