Performance analysis of a 2D photonic crystal-based biosensor for cervical and skin cancer detection with machine learning-based data classification

Key Points

• This research aims to evaluate the effectiveness of a 2D photonic crystal-based biosensor for detecting cervical and skin cancers using machine learning techniques.
• Utilized a 2D photonic crystal structure to develop the biosensor.
• Implemented machine learning algorithms for data classification of cancer samples.
• Conducted performance analysis to assess detection accuracy against conventional methods.
• Demonstrated a significant increase in detection accuracy for cervical and skin cancers compared to traditional methods.
• Achieved a classification accuracy of over 90% using machine learning approaches.