Alzheimer’s disease (AD) is a brain ailment that gradually impairs thinking and memory abilities as well as the capacity to do even the most basic tasks. A proper diagnosis of Alzheimer’s disease (AD) is crucial for patient treatment, particularly in the early stages of the illness when patients can take precautions before suffering irreparable brain damage. Many machine detection techniques are constrained by congenital observations, even though several recent research have employed computers to diagnose stages of AD. In the proposed approach, the hippocampus area is identified as a biomarker by segmenting the region using 3D deep learning algorithms and classification of stages of AD performed using 3D network-based transfer learning techniques. Thus, an efficient segmentation and classification technique for the detection of the hippocampus region and identification of the different stages of AD is determined to design a computer aided diagnostic system.