Kidney Stone Detection with CT Images Using Neural Network
DOI:
https://doi.org/10.61841/a5m3ce50Keywords:
Computed Tomography Image, CT Scan Images, Back Propagation Neural Network, BPN, Fuzzy Clustering Means, Fuzzy C-Means, Gray Level Co-occurrence Matrix.Abstract
Back Propagation Network (BPN) with image and data processing techniques are employed to implement an automated kidney stone classification. By human inspection and operators, it is impossible to produce result for large amount of dataset. CT scan and MRI produces a lot of noise and hence leads to inaccuracies. Artificial intelligent techniques through neural networks techniques have shown great importance in this field. Hence, in this project we are applying the Back-Propagation Network (BPN) for the purposes. Features are extracted using GLCM and are then classified using BPN. This project presents a segmentation method, Fuzzy CMean (FCM) clustering algorithm, for segmenting computed tomography images to detect the kidney stones in its early stages.
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