Distributed Federated Learning-Based Deep Learning Model for Privacy MRI
  Brain Tumor Detection
Lisang Zhou Meng Wang Ning Zhou
Distributed training can facilitate the processing of large medical image
datasets, and improve the accuracy and efficiency of disease diagnosis while
protecting patient privacy, which is crucial for achieving efficient medical
image analysis and accelerating medical research progress. This paper presents
an innovative approach to medical image classification, leveraging Federated
Learning (FL) to address the dual challenges of data privacy and efficient
disease diagnosis. Traditional Centralized Machine Learning models, despite
their widespread use in medical imaging for tasks such as disease diagnosis,
raise significant privacy concerns due to the sensitive nature of patient data.
As an alternative, FL emerges as a promising solution by allowing the training
of a collective global model across local clients without centralizing the
data, thus preserving privacy. Focusing on the application of FL in Magnetic
Resonance Imaging (MRI) brain tumor detection, this study demonstrates the
effectiveness of the Federated Learning framework coupled with EfficientNet-B0
and the FedAvg algorithm in enhancing both privacy and diagnostic accuracy.
Through a meticulous selection of preprocessing methods, algorithms, and
hyperparameters, and a comparative analysis of various Convolutional Neural
Network (CNN) architectures, the research uncovers optimal strategies for image
classification. The experimental results reveal that EfficientNet-B0
outperforms other models like ResNet in handling data heterogeneity and
achieving higher accuracy and lower loss, highlighting the potential of FL in
overcoming the limitations of traditional models. The study underscores the
significance of addressing data heterogeneity and proposes further research
directions for broadening the applicability of FL in medical image analysis.