Mobile Application Using CNN For Skin Disease Classification with User Privacy

Skin diseases present a complex challenge for mechanical analysis due to the inherent irregularities in skin texture, varying complexions, and the presence of hair and other surface features. The need for an accurate and automated system for skin disease detection is paramount. However, the task is compounded by issues such as dataset imbalance and stringent data privacy concerns associated with medical images. In this study, we harnessed the power of Convolutional Neural Networks (CNNs) in the domain of Medical Image Analysis (MIA) to classify skin diseases. Additionally, we employed a federated learning approach to safeguard data privacy. Our results demonstrate the remarkable performance of CNNs, achieving an accuracy score of 0.90 in skin disease classification. Building upon these findings, we propose the development of a mobile application tailored for skin disease classification, leveraging CNNs and the federated learning strategy. This mobile app offers an innovative solution for skin analysis while maintaining the highest data security and privacy standards. In conclusion, our research underscores the potential of CNNs and federated learning in the realm of skin disease classification. We introduce a promising path for the creation of an efficient mobile application for skin disease diagnosis, meeting the rigorous demands of modern medical data handling and analysis without compromising data security or privacy.