Improving Ultrasound Kidney Stone Detection Using Deep Learning
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Date
2023-04
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Addis Ababa University
Abstract
Background: Currently a CT scan is preferred over Ultrasound images for Kidney stone diagnosis. The major problem regarding diagnosis of these stones under CT imaging mo-dality is that once these patients are diagnosed as positive; there is a high chance for the stone to be formed again in the patient’s lifetime after removal. As a result, use of the CT modality repeatedly exposes the patient for unwanted radiation exposure.
Purpose of the Research: In ultrasound imaging, additive and multiplicative noises are taken as disadvantage for its imaging output. However, its ability to form real time imaging makes it preferable in many diagnosis procedures. The current study is aimed at developing an effective kidney stone detection scheme using a Convolutional Neural Network (CNN) by incorporating useful image pre-processing tools applied on Ultrasound images.
Methods: The approach implemented in the proposed kidney stone detection scheme mainly involves two stages. The first stage employed using useful pre-processing steps applied on the Ultrasound images, which include image filtering, contrast enhancement and 2-D Directional Wavelet Transforms. The second stage is employed using multiclass se-mantic segmentation CNN models, which include Deep Lab V3 , U-net and Seg-net mod-els. In order to detect multiclass regions of ultrasound kidney stone image.The performance of the models was evaluated using useful quantitative matrices.
Results and Conclusion: Results have shown that the Deep Lab V3 CNN model had greater performance than U-net and Seg-net CNN models tested in this study. The model was able to maintain a global accuracy and mean accuracy of 95.1% and 80.9% respec-tively showing its great promises in improving the detection of kidney stones based on Ultrasound images. Compared to performances reported in the literature by previous schol-ars who have developed different method of kidney stone detection algorithms, the pro-posed method has offered commendable results in terms of global accuracy and mean ac-curacy.
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Keywords
Ultrasound, deep learning, kideny stone, detection