Convolutional neural network (CNN) and federated learning-based privacy preserving approach for skin disease classification

• Published in: The Journal of supercomputing 2024-11, Vol.80 (16), p.24559-24577
• Main Authors: Divya, Anand, Niharika, Sharma, Gaurav
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Classification; Compilers; Complex formation; Computer Science; Datasets; Federated learning; Interpreters; Machine learning; Medical imaging; Medical research; Neural networks; Privacy; Processor Architectures; Programming Languages; Skin diseases; Teaching methods
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-024-06309-0

This research displays inspect a study on the classification of human skin diseases using medical imaging, with a focus on data privacy preservation. Skin disease diagnosis is primarily done visually and can be challenging due to variant colors and complex formation of diseases. The proposed solution involves an image dataset with seven classes of skin disease, a convolutional neural network (CNN) model, and image augmentation to increase dataset size and model generalization. The suggested CNN model attained an average precision of 86% and an average recall of 81% for all seven classes of skin diseases. To safeguard the privacy of the data, a federated learning method was used, in which the information was split among 500, 1000, and 2000 users. With the proposed scheme which based on CNN for disease classification and the federated learning method, the average accuracy was 82.42%, 87.26%, and 93.25% for the different numbers of clients. The findings show that it may be possible to effectively categorize skin illnesses by employing a CNN-based approach coupled with federated learning in order to achieve this goal. This would be conducted without compromising the confidentiality of patient data.