• Sarang Shirole
• Sumukh Rabade
• Rutuja Kendre
• Mrunal Choudhary
• Prof. Manisha P. Navale

Kidney stones, a common urological condition, pose significant health risks if left untreated. This synopsis introduces a project focused on developing a precise kidney stone detection system using ultrasound images and advanced machine learning techniques, specifically Convolutional Neural Networks (CNN) and Support Vector Machines (SVM), utilizing a dataset of ultrasound images. Our methodology includes ultrasound image pre-processing to enhance quality and CNNs for automatic pattern extraction. CNNs are well-suited for medical image analysis due to their feature extraction capabilities. Concurrently, SVMs improve region classification for increased accuracy. The successful completion of this project has the potential to transform kidney stone diagnosis, providing a non-invasive, cost-effective, and accurate tool for healthcare professionals. This project underscores the interdisciplinary role of artificial intelligence in healthcare, offering opportunities for advancements in disease detection and diagnosis.

Kidney Stones, Ultrasound images, Machine learning, Deep Learning, Support Vector Machines (SVM), Convolution Neural Networks (CNN), Medical image analysis

"Kidney Stone Detection with Deep Learning: A review ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.a675-a679, November-2023, Available :http://www.jetir.org/papers/JETIR2311084.pdf

"Kidney Stone Detection with Deep Learning: A review ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppa675-a679, November-2023, Available at : http://www.jetir.org/papers/JETIR2311084.pdf