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An Ensemble CNN With Bayesian Learning Model for Multiclass Classification of Brain Disease Using Adaptive Refinement

Alampally Sreedevi¹, Neravati Nagaraja Kumar², Tejaswini Panse³

¹Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana, India.

NMR in Biomedicine

|November 5, 2025

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Early brain tumor diagnosis using deep learning improves patient survival. This study introduces an adaptive refinement network and fitness-based flamingo search algorithm for accurate MRI segmentation and classification via an ensemble CNN with Bayesian learning.

Area of Science:

Medical Imaging
Artificial Intelligence
Computational Neuroscience

Background:

Brain tumors significantly impact patient survival, necessitating early and accurate diagnosis.
Traditional diagnostic methods face challenges in feature selection and classification accuracy.
Deep learning offers a promising approach for brain abnormality recognition.

Purpose of the Study:

To develop a robust deep learning model for early brain tumor diagnosis using MRI images.
To enhance segmentation accuracy using adaptive refinement network (ARN) and fitness-based flamingo search algorithm (FFSA).
To improve classification performance through an ensemble convolutional neural network (CNN) with Bayesian learning (ECNN-BL).

Main Methods:

Image segmentation using ARN, optimized by FFSA for tumor delineation.

Keywords:

adaptive refinement network brain diseases ensemble CNN with Bayesian Learning fitness‐based flamingo search algorithm multiclass classification

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Classification of segmented images using an ensemble CNN comprising VGG16, Resnet, and Xception models.

Integration of Bayesian learning to enhance the ensemble model's generalization and robustness.

Main Results:

The developed model demonstrated dependable and efficient identification of brain diseases in MRI scans.
The FFSA algorithm effectively optimized segmentation parameters, improving accuracy.
The ECNN-BL model achieved superior classification performance compared to existing methods.

Conclusions:

The proposed deep learning framework provides an efficient and accurate solution for brain tumor diagnosis.
Combining advanced segmentation and ensemble classification techniques leads to improved diagnostic outcomes.
This approach holds significant potential for clinical application in neuro-oncology.