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Related Experiment Video

Updated: Aug 14, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Hybrid Optimization Algorithm Enabled Deep Learning Approach Brain Tumor Segmentation and Classification Using MRI.

S Deepa¹, J Janet², S Sumathi³

¹Professor, Department of ECE, Panimalar Engineering College, Chennai, India. dineshdeepas1977@gmail.com.

Journal of Digital Imaging

|January 9, 2023

Summary

This summary is machine-generated.

This study introduces a novel hybrid optimization algorithm for brain tumor detection using MRI scans. The method enhances classification accuracy, aiding in timely and precise diagnosis for improved patient treatment.

Keywords:

Chronological concept Data augmentation Gaussian noise Honey badger algorithm Normalization

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Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

Brain tumors pose significant health risks, necessitating accurate detection and classification.
Magnetic Resonance Imaging (MRI) is crucial for brain tumor analysis due to its high resolution.
Effective classification requires detailed information on lesion size, type, and changes.

Purpose of the Study:

To develop an efficient hybrid optimization algorithm for brain tumor segmentation and classification.
To improve the accuracy and efficiency of brain tumor diagnosis using advanced computational methods.

Main Methods:

Feature extraction using Convolutional Neural Networks (CNN).
Classification using Deep Residual Networks (DRN).
Training the DRN with a novel Chronological Jaya Honey Badger Algorithm (CJHBA), integrating Jaya, Honey Badger Algorithm (HBA), and chronological concepts.

Main Results:

The proposed CJHBA achieved high performance on the BRATS 2018 dataset.
Maximum accuracy of 0.9210, sensitivity of 0.9313, and specificity of 0.9284 were attained.
The algorithm demonstrated effectiveness in segmenting and classifying brain tumors.

Conclusions:

The developed hybrid optimization algorithm shows significant promise for accurate brain tumor segmentation and classification.
This approach can aid medical specialists in timely and precise diagnosis, leading to better patient outcomes.