The intricate structure of the cell makes it extremely challenging and time-consuming to recognize lung cancer promptly on. Rapid proliferation of malignant cells causes cancer to spread throughout the body. One important aspect of picture processing—which is also helpful for treating lung cancer prevention—is the prediction of early-stage lung cancer cells. This suggested system uses an artificial neural network classification and algorithms for image processing to deliver a computer-based medicine diagnostic method for premature lung cancer identification. This system’s processing stage includes using masks with morphological operations as well as thresholding technique to exclude surrounding tissue and backgrounds. A number of image-enhancing algorithms are also included in this endeavor. The dimension of rate of interest (ROI) is calculated using the area-based dividing approach. The circle fit method is used to remove the desired nodule. Pulling out characteristics like span, mean vitality, area, Euler number, and ECD is part of the extracting features stage. The artificial semantic network (ANN) is trained for categorization utilizing the back breeding strategy as the final step.