• Vishal Suthar

Kidney stone detectors appear to be a significant difficulty for identifying a kidney stone illness. Identifying a kidney stone may need a method that guarantees accuracy and also is in broad usage. An automated diagnosis of kidney stone (having stone/not) utilizing coronal computed tomography (CT) pictures is suggested utilizing the deep learning (DL) method that has lately achieved considerable development in the area of artificial intelligence. The main aspect of this study is the identification of the afflicted part of the kidney stone picture. Kidney stone diagnosis is one of the sensitive issues presently. Many issues are associated with this subject, such as the poor resolution of a picture, a resemblance of kidney stone & predictions of stone in the new picture of kidney. Ultrasound pictures have poor contrast or are hard to identify & extract the area of interest. Therefore, a picture needs to go thru the preprocessing that usually includes image enhancement. In this work, we have developed the VGG-19 CNN model, which is the DL method. The VGG19 has several layers. However, as the network becomes deeper, overfitting will be an issue that needs to be addressed. This issue is addressed through data augmentation. A total of 1799 pictures were utilized by obtaining separate cross-sectional CT scans for every individual. The developed automated prototype demonstrated precision utilizing CT pictures in identifying a kidney stone. We have noticed that our model cancorrectly identify kidney stones of even tiny size. Our created DL framework produced good outcomes with this dataset and is suitable for clinical use. This research demonstrates that lately used DL techniques may be utilized to solve additional difficult issues in urology.

"Automated Kidney Stone Detection using Coronal CT images based on Deep VGG-19 Model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.c206-c218, May-2024, Available :http://www.jetir.org/papers/JETIR2405224.pdf

"Automated Kidney Stone Detection using Coronal CT images based on Deep VGG-19 Model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppc206-c218, May-2024, Available at : http://www.jetir.org/papers/JETIR2405224.pdf