Lung Cancer Detection Using MATLAB with Computer Vision Techniques

In this article, we explore how to utilize MATLAB for lung cancer detection. Lung cancer remains one of the most prevalent forms of cancer, where early detection is critical for effective treatment and improved survival rates. Over recent decades, the incidence of lung cancer has continued to rise, driving researchers to develop more accurate and reliable detection and diagnostic methods. MATLAB serves as a powerful computational tool that enables sophisticated data analysis and image processing, significantly enhancing the accuracy of lung cancer detection systems. This article demonstrates how to process pulmonary images using MATLAB's Image Processing Toolbox, implementing computer vision techniques to identify malignant lesions. We will discuss key functions such as imread() for image input, imadjust() for contrast enhancement, and morphological operations using imbinarize() and imopen() for feature extraction. The implementation includes algorithm optimization strategies focusing on improving accuracy through machine learning approaches like SVM classification with fitcsvm() or neural networks using the Deep Learning Toolbox. We'll also cover performance evaluation metrics using MATLAB's classification learner app and cross-validation techniques. This comprehensive guide provides valuable insights into MATLAB-based lung cancer detection methodologies, serving as a practical reference for future research and clinical application development.