## 5 Conclusions and Future Work  
The majority of diagnoses in dermatology are based on visual pattern recognition of morphological features. Skin imaging technology currently involves dermoscopy devices, very-high-frequency (VHF) ultrasound, and reflectance confocal microscopy (RCM). Each method of skin imaging has its advantages and limitations. Dermatologists need to choose different imaging methods according to varying conditions of skin lesions. Skin imaging technology has become a vitally important tool for the clinical diagnosis of skin diseases, and it is widely accepted and applied in the world. At the same time, machine learning-based dermoscopy is exceedingly suitable for improving the diagnosis capabilities of dermatologists. Accordingly, this study proposed an intelligent dermoscopy device, which can be used by health practitioners for the clinical diagnosis of skin tumors. This study offers a continuous improvement in classification accuracy by developing a more robust solution through the adaptability mechanism. To ensure adaptability, this study proposed an adaptive federated machine learning-based model that can correctly classify the dermoscopy images for skin disease classification, capable of learning new features (new samples acquired during the classification task through the dermoscopy device). This study used the previously proposed online training and online classifier update (k-means clustering method for new training dataset formation). However, this study found that using the clustering-based mechanism to distinguish different classes for similar features resulted in some classification degradation after adaptation. Hence, a supervised learning mechanism should be used for new dataset formation. The proposed framework showed satisfactory performance for both the cloud and the edge models. The results demonstrated adequate classification performance (in terms of accuracy) for the edge models, which is essential for clinical trials. Furthermore, a patient-level mobile application is also offered to help patients locate the nearest dermatologist and to provide the necessary information regarding the skin disease to prevent negligence. However, this module was not practically tested herein and will be addressed in a future study. The authors also aim to develop a prototype (hardware) intelligent dermoscopy device for dermatologists (using the proposed federated machine learning-based model), which will be tested clinically. Conceptualization, M.A.H. and S.M.J. ; methodology, M.A.H. and S.M.J. ; software, S.M.J. ; validation, M.A.H. and S.S.; formal analysis, M.A.H. and S.M.J. ; investigation, M.A.H. and S.M.J. ; resources, Manzoor Ahmed Hashmani and S.S.H.R. ; data curation, S.M.J. ; writing--original draft preparation, S.M.J. ; writing--review and editing, M.A.H. and S.S.H.R. ; visual  
Figure 14: Training and validation accuracy and loss for new sample training in edge models; (**a**) validation accuracy (val_acc), testing accuracy (test_acc), validation loss (val_loss), and testing loss (test_loss) when new samples were trained at epoch 3; (**b**) validation accuracy (val_acc), testing accuracy (test_acc), validation loss (val_loss), and testing loss (test_loss) when new samples were trained at epoch 6.  
ization, S.M.J. ; supervision, M.A.H. ; project administration, M.A.H. and S.M.J. ; funding acquisition, M.A.H. All authors have read and agreed to the published version of the manuscript. This research study was conducted at the Universiti Teknologi PETRONAS (UTP), Malaysia, as a part of the research project \"A novel approach to mitigate the performance degradation in big data classification model\" under the matching grant scheme (Cost Centre: 015 ME0-057). Not applicable. The ISIC 2019 challenge dataset is available at [[https://challenge2019.isic-archive.com](https://challenge2019.isic-archive.com)]([https://challenge2019.isic-archive.com](https://challenge2019.isic-archive.com)).