Case based Reasoning Technique in Digital Diagnostic System for Lung Cancer Detection

An efficient method for creating digital diagnostic systems for the early diagnosis of lung cancer is case-based reasoning (CBR). Artificial intelligence in the form of CBR employs knowledge from the past, or "cases," to answer contemporary problems. In the context of lung cancer detection, CBR systems use patterns and relationships identified in previous diagnostic cases to make predictions about new cases. CBR systems have several advantages over other approaches to lung cancer detection, including increased accuracy and reduced false positive rates. CBR systems can also be easily updated with new information, allowing them to continually improve over time. Despite these advantages, there are also challenges associated with the use of CBR in lung cancer detection. For example, CBR systems may be biased by the training data they are provided, leading to incorrect predictions. Additionally, CBR systems may have difficulty handling complex relationships and patterns in the data, leading to reduced accuracy. The use of CBR techniques in digital diagnostic systems for lung cancer detection has the potential to provide more accurate and reliable diagnostic results, while also reducing the time and resources required for traditional diagnostic methods. To fully exploit the potential of CBR for lung cancer detection, additional study and development in this field are required.