Cell Edge Detection and Cancer Cell Recognition System

The cell edge detection and cancer cell recognition system operates within the MATLAB environment, utilizing sophisticated image processing techniques. This system extracts critical features by analyzing cellular boundaries and employs these characteristics to identify cancerous cells. In MATLAB, we implement various image processing and analysis methods, including edge detection algorithms like Canny or Sobel operators, morphological operations for feature enhancement, and machine learning classifiers for pattern recognition. The primary objective is to provide an automated approach for detecting and identifying cancer cells, assisting medical professionals and researchers in achieving more accurate cancer diagnosis and treatment planning. When operating this system, users input cellular images, and the system outputs classification results indicating whether cells are cancerous based on extracted morphological and textural features. Key implementation steps typically involve image preprocessing (noise reduction, contrast enhancement), edge detection using gradient-based methods, feature extraction (shape descriptors, texture analysis), and classification using SVM or neural networks. Through this system, we gain deeper insights into cancer development mechanisms while providing substantial support for cancer research and therapeutic strategies.