Optimize Weight sharing for Aggregation Model in Federated Learning Environment of Brain Tumor classification

Abstract

Clinical diagnosis and therapy of brain tumors are greatly aided by proper classification of the tumors. Brain tumors can be diagnosed more quickly and accurately if radiologists use deep learning to help the specialist and doctors examine the enormous volume of brain MRI Images. Large datasets are required in training process, and whole of such data must be centralized for be handled by such techniques. It is sometimes impossible to collect and distribute patient data on a centralized data server because of medical data privacy regulations. In this paper, federated learning (FL) is proposed, in which data is non-shareable because of patient privacy issues. Using the FL approach, we have proposed two methods of aggregation; first, which concerns ranking the weight percentage of each client, and Second average weights method. and to evaluate the suggested model, we have compared the performance of the ranking weights percentage method with the average weights of proposed CNN and pre-training (VGG-16) in the FL environment in addition to SVM and VGG-16 . The experiments result was applied on two datasets, it shows our model accuracy result is very effective when using the ranking weight percentage method as compared with other methods, it achieves accuracy (98%) on datasets (BT_large-1c) and achieve (97.14%) on the dataset (BT-large-2c).

A brain tumour classification on the magnetic resonance images using convolutional neural network based privacy‐preserving federated learning

“…In order to assess the performance of the proposed model, it was examined how well support vector machine (SVM) and VGG16 performed in the FL environment together with the average weights of proposed CNN and VGG16. The experimental findings were 98% accuracy on BT_large‐1c and 97.14% on BT‐large‐2c for rating weight percentage 29 . Viet et al applied FedAVG with VGG16, ResNet50, ConvNext and MaxViT to the Figshare dataset, and ConvNeXt obtained 98.69% accuracy on independently and identically distributed (IID) data 30 .…”

“…The experimental findings were 98% accuracy on BT_large-1c and 97.14% on BT-large-2c for rating weight percentage. 29 Viet et al applied FedAVG with VGG16, ResNet50, ConvNext and MaxViT to the Figshare dataset, and ConvNeXt obtained 98.69% accuracy on independently and identically distributed (IID) data. 30 Bhati and Samed proposed a framework for evaluating data based on the blockchain in FL.…”