IMPLEMENTATION AND ANALYSIS OF KIDNEY STONE DETECTION USING RNN

Abstract: Kidney stones, also known as renal calculi, are solid deposits that form in the kidneys due to minerals and salts. They can range from tiny grains to larger formations and often lead to intense pain and complications. Dehydration, dietary factors, genetics, and certain medical conditions are common causes. Symptoms of kidney stones include severe back or side pain, blood in the urine, frequent urination, and nausea. Diagnosis involves looking at medical history, doing a physical exam, using imaging tests like non-contrast CT scans or ultrasound, and performing laboratory tests such as urinalysis. Treatment options depend on the characteristics of the stone and may include pain medication, increased fluid intake, medical expulsion therapy, shock wave lithotripsy, ureteroscopy, or surgery. It's important to have regular check-ups and make lifestyle changes to prevent kidney stone recurrence. Early detection and proper management are key to reducing symptoms and preventing complications. Machine learning can help in detecting kidney stones by analyzing medical imaging data, like CT scans or ultrasound images.