Kidney Disease Classification and Diagnosis: A Comprehensive Review of Current AI Techniques

Accurate and early diagnosis of kidney disease (KD) is crucial for effective treatment and improved patient outcomes. computer aided and AI techniques offers a promising solution in this domain, analyzing medical images to identify disease patterns. This research paper explores the potential of ML, particularly deep learning (DL) and transfer learning, for classifying kidney conditions from CT scans. We review existing research to identify the most effective ai algorithms for this task, evaluate performance metrics used to assess accuracy, and explore challenges like data availability and interpretability. By discussing future directions, including integration of additional data sources, this paper aims to provide a comprehensive analysis of AI techniques-based KD classification, paving the way for more accurate and efficient diagnostic tools.