Lichen Planus and Psoriasis are chronic inflammatory skin diseases that must be diagnosed and managed early to prevent a more severe level and guarantee better quality of life for the affected patients. Current diagnostic techniques for Lichen Planus and Psoriasis have been found to take a lot of time and greatly rely on the level of knowledge a dermatologist has. In an attempt to mitigate these risks, this paper proposes a ResNet- 50 based deep learning model for the creation of a user-friendly Android-based application for the management of Lichen Planus and Psoriasis. The proposed system uses the ResNet-50 deep learning model to classify the skin lesion images in the following classes: Lichen Planus, Psoriasis, and normal skin. This system also uses a transfer learning technique to optimize the performance of the model and simplify the complexity of the learning process. In the pre-processing of the image dataset, the following image processing techniques are used for better performance and robustness.For the practical application of the model, it is embedded into an Android-based application that enables the user to capture or upload images of the skin lesions using their devices. This provides the user with immediate prediction results, as well as important information on diseases, prevention, and basic management. The interface of the system is simple, intuitive, and user-friendly, making it accessible to both patients and HCPs. The framework allows for the early detection of diseases, reduces the need for manual screening, and monitors skin conditions in real-time. It also enables the application of efficient methods for the management of skin diseases based on the integration of deep learning techniques and mobile application technology. Thus, the specific role of the mobile health systems based on the ResNet-50 for the management and improvement of skin disease diagnosis has been presented in the specific article.