Machi̇ne learning-based model deployment in mobile health applications and device-cloud deployment

Mobile Health allows health applications to be portable and enables mobile phone users to instantly access health solutions. In recent years, models developed with machine learning approaches have played an important role in healthcare in diagnosing and monitoring various diseases. However, the integration and execution of machine learning models, especially those based on image processing and deep learning, on mobile devices can be challenging due to the device`s limited processing resources and limited storage capacity. This thesis describes the step-by-step development of a machine learning-based model, and its integration into mobile devices and cloud environments. Predicting skin disease has been chosen as a use-case mHealth model due to its usability. A MobilNet architecture has been developed due to its suitability for mobile applications. Transfer learning technique, data augmentation and different loss functions have been considered to improve the model`s performance. The trained mHealth model is then integrated into both device and cloud. All the technical details of the process and a comparison of device cloud integration are provided.