What are the Challenges in Implementing CNNs for Cancer Detection?
Despite their potential, there are several challenges in implementing CNNs for cancer detection:
Data Quality: The performance of CNNs heavily relies on the quality and quantity of training data. Poorly annotated or insufficient data can hinder model accuracy. Interpretability: CNN models are often considered "black boxes," making it challenging to interpret how they arrive at specific decisions. Regulation and Validation: Ensuring that CNN models meet regulatory standards and undergo rigorous validation is crucial for clinical adoption. Integration: Integrating CNNs into existing medical workflows requires significant technical and infrastructural investments.
