A Federated Learning-Based Approach for Classification of Histopathology Images - Yenilmez, Musa; Aydin, Ilhan (Institute of Electrical and Electronics Engineers, 2024)
Nowadays, deep learning and machine learning methods are widely used in computer-aided diagnosis. In healthcare studies, they provide reliable and effective solutions for diagnosis and detection. However, important problems such as ensuring the privacy of patient data arise in these studies. In this study, the HAM10000 dataset was used, and the classification of histopathological images was conducted using the Federated Learning (FL) approach and deep learning methods to ensure data privacy and security. As a result of the tests performed, the classification accuracy rate of the proposed method was observed to be 82.04%. © 2024 IEEE.
14th International Conference on Advanced Computer Information Technologies, ACIT 2024 -- 19 September 2024 through 21 September 2024 -- Ceske Budejovice -- 203443
Keywords: computer-aided diagnosis; data privacy; federated learning; histopathology; medical image processing