Abstract
Half of the global population has or has had shoulder fractures due to routine activities, whether intentional or unintentional. Designing a user-friendly program to input Shoulder fracture images into the model and execute it is a significant problem in the area of Shoulder Fracture Diagnosis Research. The machine is capable of predicting shoulder fractures, but it is unable to anticipate the specific subtypes of fractures that may result from other occurrences or disorders. This research aims to create an innovative Deep Convolution Neural Networks system for accurately predicting the diagnosis of shoulder fractures, in order to facilitate appropriate treatment. Our goal is to provide these services to assist individuals or groups in overcoming the delayed detection of shoulder fractures and eliminating the need for physical diagnosis of shoulder fractures in hospitals. The objective of designing a unique categorizing system is to provide a collection of test case inputs that ensure comprehensive coverage over the test area at a predetermined level. This yields a collection of test cases that prioritize the execution of the feature without considering the specific implementation details.