This study examines lung cancer detection and classification methods in depth and suggests a unique deep learning-based process. The methodology includes image acquisition, preprocessing, thresholding, feature extraction, rank-correlated CNN layers, kernel-based SVM classifier, and model evaluation. Obtaining access to medical images from numerous sources while assuring their quality and confidentiality is what image acquisition implies. The efficacy of deep learning is enhanced by the correction of image defects through preprocessing. Thresholding divides zones of interest through the use of adaptive and global thresholding techniques. Feature extraction is the term used to describe the process of removing informative elements from visual attributes.