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Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

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Fuzzy guided ensemble inference system for brain tumor classification.

M Ashwin Kumar¹, G Manikandan¹, L Richard¹

¹School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.

|November 2, 2025

Summary

This summary is machine-generated.

Early brain tumor detection is crucial for effective treatment. A new Fuzzy Guided Ensemble Inference System (FGEIS) using Convolutional Neural Networks (CNNs) accurately identifies brain tumors from MRI images, achieving 99.85% accuracy.

Keywords:

Brain Tumor Convolutional Neural Network (CNN)Densenet Ensemble learning Fuzzy Logic MRI scans Mobilenet Resnet VGG

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Brain tumors, both benign and malignant, cause increased intracranial pressure and severe symptoms.
Late-stage brain tumor treatment is challenging, highlighting the need for early detection.

Purpose of the Study:

To develop an automated system for early brain tumor identification from MRI images.
To enhance diagnostic accuracy and clinical utility through an advanced AI model.

Main Methods:

Proposed a Fuzzy Guided Ensemble Inference System (FGEIS), a fuzzy logic-based ensemble method.
Integrated four Convolutional Neural Network (CNN) architectures: Densenet, Resnet, VGG, and Mobilenet.
Utilized ensemble learning and fuzzy logic for improved tumor classification from MRI scans.

Main Results:

The FGEIS model achieved a high classification accuracy of 99.85%.
Ensemble models demonstrated superior performance compared to individual CNN architectures.
The system effectively combines feature learning, reuse, localization, and computational efficiency.

Conclusions:

The FGEIS model shows significant potential for accurate and early brain tumor detection in clinical settings.
AI-driven ensemble methods offer a promising approach to improving diagnostic outcomes in neuro-oncology.
Early identification via advanced imaging analysis can lead to better patient management and prognosis.