Lung cancer is among the most significant fatal cancers, and according to statistics, it is the second disease-causing high mortality rate among women/men. Early detection can lead to survival with the proper treatment process. The detection of Lung cancer manually consumes money and time and is inefficient in several cases. In this proposed system, Lung cancer is segmented and detected using an artificial neural network classification method in mammogram images. “Lung cancer GUI”- A MATLAB-based Graphical User Interface (GUI) is created in the proposed system. MIAS database mammogram images are uploaded to the “Lung cancer GUI,” and the image is preprocessed using the adaptive median filtering method to remove noises. Then the images are segmented and extracted features using the Gaussian mixture model (GMM) with a gray level co-occurrence matrix. These extracted features are trained and classified utilizing the probabilistic neural network (PNN) classification approach. The proposed system identified and classified the images into benign, malignant, or normal types with 96.67% accuracy, 97.0% precision, 96.0% sensitivity, and 97.98% specificity.