Enhancing Lung Cancer Diagnosis with MATLAB and GLCM: A Robust Image Processing Approach

Rezumat

This study presents a novel approach to the diagnosis of lung cancer by utilizing MATLAB to integrate modern image processing. Our method simplifies recognizing malignant phases by focusing on the examination of minute micro structural differences and utilizing MATLAB's Grey Degree Co-occurrence Matrix (GLCM) properties for accurate texture pattern extraction. The study investigates several wavelengths in conjunction with multispectral imaging to capture subtle differences across various cancer stages. Through the integration of advanced computational techniques with spectral insights, our methodology presents a comprehensive plan for enhancing photo sensitizer responses to enhance distinction. The main objective is to greatly increase the precision of tumour cell identification, encouraging early diagnosis and transforming therapeutic approaches for improved patient outcomes. This study advances the area of lung cancer identification and presents a viable path forward for enhancing clinical practice efficacy and diagnostic capacities.