Diabetes mellitus and lung cancer are two of the most common fatal diseases in the world, causing considerable deaths every year. However, it is not easy to detect diabetes mellitus and lung cancer efficiently - needing professional medical instruments such as a CT and a qualified individual to perform the Fasting Plasma Glucose test. Considering the risks and various inconveniences with conventional diagnosis methods, noninvasive approaches based on computerized analysis are desired. The aim of this paper is to distinguish patients with diabetes mellitus, lung cancer from healthy people simultaneously by analyzing facial images through the stacked sparse autoencoder. Experimental results on a dataset containing 450 healthy samples, 284 diabetes and 175 lung cancer patients produced the F1-score of 93.57%, 97.54%, 81.56% for detecting healthy, diabetes and lung cancer, respectively, validating the effectiveness of our proposed method.