Ensemble Federated Learning Approach for Diagnostics of Multi-Order Lung Cancer
Umamaheswaran Subashchandrabose,
Rajan John,
Usha Veerasamy Anbazhagu,
Vinoth Kumar Venkatesan,
Mahesh Thyluru Ramakrishna
Affiliations
Umamaheswaran Subashchandrabose
Department of Artificial Intelligence and Machine Learning, New Horizon College of Engineering, Bangalore 560103, India
Rajan John
Department of Computer Science, College of Computer Science and Information Technology, Jazan University, Jazan 45142, Saudi Arabia
Usha Veerasamy Anbazhagu
Department of Computing Technologies, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chennai 603203, India
Vinoth Kumar Venkatesan
School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore 632014, India
Mahesh Thyluru Ramakrishna
Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-Be University), Bangalore 560066, India
The early detection and classification of lung cancer is crucial for improving a patient’s outcome. However, the traditional classification methods are based on single machine learning models. Hence, this is limited by the availability and quality of data at the centralized computing server. In this paper, we propose an ensemble Federated Learning-based approach for multi-order lung cancer classification. This approach combines multiple machine learning models trained on different datasets allowing for improvising accuracy and generalization. Moreover, the Federated Learning approach enables the use of distributed data while ensuring data privacy and security. We evaluate the approach on a Kaggle cancer dataset and compare the results with traditional machine learning models. The results demonstrate an accuracy of 89.63% with lung cancer classification.
