• Lalitha Bhagya Prathyusha K
• Pavithra K
• Harshitha Reddy B
• Ashida Sk
• Kiran Lal V, Sahithi P

Kidney stone problem (nephrolithiasis) is a common type of urological disease with a high recurrence rate. This disease is a progressive disease that damage the kidneys leading to be permanent and undone problem. Therefore, it is vital to identify kidney stone disease before the permanent damage is done. If the stone problem is caught in the early stage, kidney disease can be treated very effectively. So, stone diagnosis is vital not only for treatment of kidney disease but also in management of recurrent stone formation. Hence early detection of kidney stone is essential Ultrasound imaging is one of the available imaging techniques used for diagnosis of kidney abnormalities, which may be like change in shape and position and swelling of limb. During surgical processes it is vital to recognize the true and precise location of kidney stone. The detection of kidney stones using ultrasound imaging is a highly challenging task as they are of low contrast and contain speckle noise. This challenge is overcome by employing suitable image processing techniques. The ultrasound image is first preprocessed to get rid of speckle noise using the image restoration process. The restored image is smoothened using Gabor filter and the subsequent image is enhanced by histogram equalization. The preprocessed image is achieved with level set segmentation to detect the stone region. Segmentation process is employed twice for getting better results; first to segment kidney portion and then to segment the stone portion, respectively. The results are analyzed using MLP-BP ANN algorithms for classification and its type of stone.

Ultrasound image, Image processing, Image segmentation, Artificial neural networks

"AUTOMATED SYSTEM FOR KIDNEY STONE DETECTION USING DEEP LEARNING MODELS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.299-304, June-2024, Available :http://www.jetir.org/papers/JETIRGJ06047.pdf

"AUTOMATED SYSTEM FOR KIDNEY STONE DETECTION USING DEEP LEARNING MODELS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp299-304, June-2024, Available at : http://www.jetir.org/papers/JETIRGJ06047.pdf