Application of Neural Networks and Machine Learning in Image Recognition

Artificial neural networks find extensive applications in various fields, including complex robotics, computer vision, and classification tasks. They are designed to mimic the highly complex, nonlinear, and parallel computational abilities of the human brain. Just like neurons in the brain, artificial neural networks can be organized to perform rapid and specific computations, such as perception and motor control. Drawing insights from the behavior of biological neural networks and their learning and adaptive capabilities, these technical counterparts have been developed to simulate the properties of biological systems. This paper concentrates on two main areas. Firstly, it explores the approximation of image recognition for healthy individuals using artificial neural networks. Secondly, it investigates the identification of kidney conditions associated with common kidney diseases that affect the global population. Specifically, the paper examines polycystic kidney disease, kidney cysts, and kidney cancer. The ultimate goal is to utilize machine learning algorithms to aid in diagnosing kidney diseases by analyzing various samples.