Lung cancer, particularly in its advanced stages, presents challenges in achieving high cure rates. Efficient early detection holds the key to significantly improving survival rates. Recognizing the critical importance of early identification, this study proposes a two-phase technique aimed at early lung cancer detection. The first phase involves the immediate importation of lung CT scans into the framework. Subsequently, the image lay-out phase is executed through explicit image management procedures. The proposed approach incorporates several advancements, including feature extraction, neural organization identification, pre-processing, binarization, thresholding, division, and image capture. During the feature extraction process, specific critical qualities are systematically removed from the segmented images. Simultaneously, the binarization method modifies matched images and aligns them with edge views. These integrated advancements collectively contribute to a comprehensive and innovative approach for the early detection of lung cancer. Achieving early detection through this method holds significant promise for enhancing lung cancer survival rates and, consequently, contributing to the overall improvement of human health outcomes.