An adaptive federated machine learning-based intelligent system for skin disease detection: A step toward an intelligent dermoscopy device

• Main Authors: Hashmani, M.A., Jameel, S.M., Rizvi, S.S.H., Shukla, S.
• Format: Article
• Published: MDPI AG 2021
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102700033&doi=10.3390%2fapp11052145&partnerID=40&md5=98f1af80a16ae486ed27393b99701927; http://eprints.utp.edu.my/23933/

The prevalence of skin diseases has increased dramatically in recent decades, and they are now considered major chronic diseases globally. People suffer from a broad spectrum of skin diseases, whereas skin tumors are potentially aggressive and life-threatening. However, the severity of skin tumors can be managed (by treatment) if diagnosed early. Health practitioners usually apply manual or computer vision-based tools for skin tumor diagnosis, which may cause misinterpretation of the disease and lead to a longer analysis time. However, cutting-edge technologies such as deep learning using the federated machine learning approach have enabled health practitioners (dermatologists) in diagnosing the type and severity level of skin diseases. Therefore, this study proposes an adaptive federated machine learning-based skin disease model (using an adaptive ensemble convolutional neural network as the core classifier) in a step toward an intelligent dermoscopy device for dermatologists. The proposed federated machine learning-based architecture consists of intelligent local edges (dermoscopy) and a global point (server). The proposed architecture can diagnose the type of disease and continuously improve its accuracy. Experiments were carried out in a simulated environment using the International Skin Imaging Collaboration (ISIC) 2019 dataset (dermoscopy images) to test and validate the model's classification accuracy and adaptability. In the future, this study may lead to the development of a federated machine learning-based (hardware) dermoscopy device to assist dermatologists in skin tumor diagnosis. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.