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Updated: Jan 9, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Boosting brain tumor detection with an optimized ResNet and explainability via Grad-CAM and LIME.

K Afnaan¹, C G Arunbalaji¹, Tripty Singh²

¹Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bangalore, Karnataka, India.

Brain Informatics

|December 5, 2025

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This summary is machine-generated.

This study enhances brain tumor detection using Convolutional Neural Networks (CNNs) by integrating explainability techniques. An enhanced ResNet model achieved high accuracy, improving AI diagnostics in medical imaging.

Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Healthcare
Computational Neuroscience

Background:

Accurate brain tumor detection is crucial for patient outcomes.
Convolutional Neural Networks (CNNs) show promise but lack clinical interpretability.
Challenges like class imbalance and overfitting hinder CNN adoption in medical diagnostics.

Purpose of the Study:

To improve the accuracy, generalization, and interpretability of CNNs for brain tumor detection.
To address the clinical adoption barrier of CNNs by integrating Explainable AI (XAI).
To develop reliable AI-assisted diagnostic tools for medical image classification.

Main Methods:

Implemented architectural enhancements and dynamic learning rate modifiers for CNNs.

Keywords:

Brain tumor classification Convolutional Neural Networks (CNN)Explainable AI (XAI)Grad-CAM

Related Experiment Videos

Last Updated: Jan 9, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Applied XAI techniques, including Grad-CAM and LIME, to CNN models.

Conducted experiments on three public multiclass brain tumor datasets to validate generalizability.

Main Results:

An enhanced ResNet model achieved superior performance, with test accuracies ranging from 99.36% to 99.65%.
Architectural improvements (unfreezing layers, integrating blocks, pooling, dropout) refined features and reduced overfitting.
XAI techniques successfully highlighted clinically relevant regions in MRI scans, enhancing model transparency.

Conclusions:

The study demonstrates a significant advancement in AI-assisted brain tumor detection through enhanced CNNs and XAI.
The proposed approach offers a reliable and interpretable solution for medical image classification.
These findings pave the way for increased clinical adoption of AI in neuro-oncology diagnostics.