Bone is the most important part of our body which holds the whole structure of human body. The long bone situated in the upper arm of human body between the shoulder and elbow junction is known as “Humerus”. Humerus works as a structural support of the muscles and arms in the upper body which helps in the movement of the hand and elbow. Therefore, any fracture in humerus disrupts our daily lives. The manual fracture detection process where the doctors detect the fracture by analyzing X-ray images is quite time consuming and also error prone. Therefore, we have introduced an automated system to diagnose humerus fracture in an efficient way. In this study, we have focused on deep learning algorithm for fracture detection. In this purpose at first, 1266 X-ray images of humerus bone including fractured and non-fractured have been collected from a publicly available dataset called “MURA”. As a deep learning model has been used here, data augmentation has been applied to increase the dataset for reducing over-fitting problem. Finally, all the images are passed through CNN model to train the images and classify the fractured and non-fractured bone. Moreover, different pretrained model has also been applied in our dataset to find out the best accuracy. After implementation, it is observed that our model shows the best accuracy which is 80% training accuracy and 78% testing accuracy comparing with other models.