Numerous ailments are plaguing people today. These health issues cannot be ignored because they pose a threat to our lives. Additionally, the healthcare industry is totally unique compared to other area. Patients, regardless of cost, expect the greatest level of care and services in this high priority industry. Most often, medical professionals analyses the interpretations of medical data. Due to subjectivity and the complexity of the medical images, a medical expert's ability to interpret images is relatively limited. In addition, there are other problems with medical images, including low contrast, speckle noise, gaussian noise, and other artefacts. Therefore, it is imperative to have high image quality to extract relevant information from images for precise disease detection. Early detection and prevention are therefore needed to help people avoid these types of health issues. As a result of its success in other real-world applications, deep learning is viewed as offering innovative and precise solutions for medical imaging and is recognized as a significant technique for upcoming applications in the healthcare industry. The objective of this paper is discussing Convolutional neural networks (CNN), a deep learning algorithm. Additionally, CNN's goals and objectives are revealed in different medical terms especially in kidney stone problem diagnosis.