Privacy preserved collaborative transfer learning model with heterogeneous distributed data for brain tumor classification
Correct identification of tumor in brain images is critical for treatment. In the medical domain,
class distributions of recorded data could differ with locations and require high levels of …
class distributions of recorded data could differ with locations and require high levels of …
Federated Learning in Medical Image Analysis: A Systematic Survey
FR da Silva, R Camacho, JMRS Tavares - Electronics, 2023 - mdpi.com
Medical image analysis is crucial for the efficient diagnosis of many diseases. Typically,
hospitals maintain vast repositories of images, which can be leveraged for various purposes …
hospitals maintain vast repositories of images, which can be leveraged for various purposes …
[PDF][PDF] Federated Learning on Internet of Things: Extensive and Systematic Review.
The proliferation of IoT devices requires innovative approaches to gaining insights while
preserving privacy and resources amid unprecedented data generation. However, FL …
preserving privacy and resources amid unprecedented data generation. However, FL …
An effective ensemble learning approach for classification of glioma grades based on novel MRI features
The preoperative diagnosis of brain tumors is important for therapeutic planning as it
contributes to the tumors' prognosis. In the last few years, the development in the field of …
contributes to the tumors' prognosis. In the last few years, the development in the field of …
IPC-CNN: A Robust Solution for Precise Brain Tumor Segmentation Using Improved Privacy-Preserving Collaborative Convolutional Neural Network
Brain tumors, characterized by uncontrollable cellular growths, are a significant global
health challenge. Navigating the complexities of tumor identification due to their varied …
health challenge. Navigating the complexities of tumor identification due to their varied …
[HTML][HTML] FL-SiCNN: An improved brain tumor diagnosis using siamese convolutional neural network in a peer-to-peer federated learning approach
AN Onaizah, Y Xia, K Hussain - Alexandria Engineering Journal, 2025 - Elsevier
Artificial Intelligence has been an essential component for successful data-driven medical
applications. Considering today's conditions, Deep Learning holds the leading role in …
applications. Considering today's conditions, Deep Learning holds the leading role in …
A Privacy-Protected Federated Learning with Cross-silo Brain Tumour Dataset for Glioma Detection
Brain tumours are abnormal growths of cells within the brain or the central spinal canal. The
occurrence of this disease in a critical location can cause significant neurological …
occurrence of this disease in a critical location can cause significant neurological …
Fed-ComBat: A Generalized Federated Framework for Batch Effect Harmonization in Collaborative Studies
In neuroimaging research, the utilization of multi-centric analyses is crucial for obtaining
sufficient sample sizes and representative clinical populations. Data harmonization …
sufficient sample sizes and representative clinical populations. Data harmonization …
Optimizing Privacy and Efficiency in Brain Tumor Classification through Advanced Non-IID Federated Deep Learning
M Samsuzzaman, RH Sezan, A Golder… - … on Information and …, 2024 - ieeexplore.ieee.org
Recent advancements in Artificial Intelligence (AI) have significantly transformed various
fields, especially medical diagnostics. However, centralized deep learning models face …
fields, especially medical diagnostics. However, centralized deep learning models face …
Federated Learning for Predictive Healthcare Analytics: From theory to real world applications
In the contemporary landscape, machine learning has a pervasive impact across virtually all
industries. However, the success of these systems hinges on the accessibility of training …
industries. However, the success of these systems hinges on the accessibility of training …