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Intracranial Implantation with Subsequent 3D In Vivo Bioluminescent Imaging of Murine Gliomas

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IV3TM: Inception V3 enabled bidirectional long short-term memory network for brain tumor classification.

Afnan M Alhassan¹, Nouf I Altmami¹

¹Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Saudi Arabia.

|October 24, 2025

Summary

This summary is machine-generated.

This study introduces a novel Inception V3 enabled Bidirectional Long Short Term Memory Network (IV3TM) for improved brain tumor classification using Magnetic Resonance Imaging (MRI). The IV3TM model enhances diagnostic accuracy for brain tumors.

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Published on: April 13, 2013

Area of Science:

Neurology
Medical Imaging
Artificial Intelligence

Background:

Brain tumors are life-threatening neurological conditions requiring early diagnosis for effective treatment.
Deep Learning methods have advanced medical image classification but face challenges with brain tumor complexities and MRI variability.
Accurate brain tumor classification is crucial for patient survival rates.

Purpose of the Study:

To propose an advanced deep learning model, the Inception V3 enabled Bidirectional Long Short Term Memory Network (IV3TM), for brain tumor classification.
To enhance the performance of brain tumor classification by employing novel preprocessing and data augmentation techniques.
To improve the accuracy and reliability of brain tumor diagnosis from Magnetic Resonance Imaging (MRI) data.

Main Methods:

Implemented an iterative weighted-mean Filter for preprocessing to address bias field fluctuations, noise, and blurring in MRI images.
Utilized data augmentation to increase the size and diversity of the training dataset.
Developed the IV3TM model by integrating Inception V3 and Bidirectional Long Short Term Memory (BiLSTM) to capture sequential dependencies in MRI data, using SqueezeNet for image segmentation.

Main Results:

The proposed IV3TM model demonstrated effectiveness in brain tumor classification.
Evaluated performance using metrics such as specificity, accuracy, precision, F1-score, and sensitivity.
Assessed model error using root mean square error (RMSE), showing encouraging results on Brain MRI and Figshare datasets.

Conclusions:

The IV3TM model offers a promising approach for accurate brain tumor classification from MRI scans.
The integration of Inception V3 and BiLSTM effectively learns intricate structural relationships in brain MRI data.
The proposed method shows potential for aiding neurologists in timely and precise brain tumor diagnosis.