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    <responseDate>2026-10-14T18:56:31Z</responseDate>
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            <header>
                <identifier>oai:data.mendeley.com/x69nzw9zzw.2</identifier>
                <datestamp>2026-05-29T12:28:51Z</datestamp>
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            <metadata><oai_dc:dc xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
    <dc:creator id="https://orcid.org/0009-0005-6740-1380">Siam, Masrafe Bin Hannan</dc:creator>
    <dc:title>Brain tumor MRI image for Fedarated Learning</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>This dataset contains 10,417 de-identified, single-channel (greyscale) brain MRI slices. It is designed to support machine learning and computer vision research for robust, four-class brain tumor classification, particularly contributing to innovations in privacy-preserving federated learning and explainable AI (XAI) frameworks.

Dataset Composition:
The dataset is organized into four distinct, clinically meaningful categories based on pathology.
⦁Glioma tumor (2,547 images): MRI slices exhibiting glioma tumors.
⦁Meningioma tumor (2,712 images): MRI slices exhibiting meningioma tumors.
⦁Pituitary tumor (2,658 images): MRI slices exhibiting pituitary tumors.
⦁No tumor (2,500 images): Healthy, tumor-free brain MRI slices.

Preprocessing:
⦁Slices are harmonized to 224x224 pixels and loaded as single-channel grayscale images.
⦁A unified preprocessing pipeline was applied, including resizing, optional center/zero-padding, and per-image normalization to ensure consistency.
⦁For federated learning simulations, the dataset is pre-partitioned across four clients and further divided into an 80/10/10 (train/validation/test) split conducted patient-wise to prevent data leakage.

This dataset can be effectively used for:
⦁Multiclass image classification and brain tumor recognition tasks.
⦁Deep learning model development, including parameter-efficient CNNs, deeper CNNs, and hybrid architectures.
⦁Benchmarking synchronous Federated Averaging (FedAvg) and privacy-preserving multi-site training methodologies.
⦁Evaluating quantitative explainability (XAI) metrics, such as Deletion AUC and Grad-CAM++ visualizations.

File Information:
⦁Total Images: 10,417 
⦁Image Format: Grayscale MRI slices 
⦁Resolution: 224x224 pixels 
⦁Data Structure: Distributed across 4 distinct client partitions.</dc:description>
    <dc:subject>Machine Learning</dc:subject>
    <dc:subject>Brain Tumor</dc:subject>
    <dc:subject>Deep Learning</dc:subject>
    <dc:subject>Federated Learning</dc:subject>
    <dc:contributor id="https://orcid.org/0009-0007-9043-3545">KHAN, RAYHAN</dc:contributor>
    <dc:type>Dataset</dc:type>
    <dc:identifier>doi:10.17632/x69nzw9zzw.2</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/x69nzw9zzw.2</dc:identifier>
    <dc:rights>Creative Commons Attribution 4.0 International</dc:rights>
    <dc:rights>http://creativecommons.org/licenses/by/4.0</dc:rights>
    <dc:relation>https://data.mendeley.com/datasets/x69nzw9zzw</dc:relation>
    <dc:date>2026-05-29T12:28:51Z</dc:date>
</oai_dc:dc></metadata>
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