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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Automated Classification of Brain Tumors from Magnetic Resonance Imaging Using Deep Learning.

Zahid Rasheed¹, Yong-Kui Ma¹, Inam Ullah²

¹School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China.

|May 16, 2023

Summary

This summary is machine-generated.

This study introduces a novel Convolutional Neural Network (CNN) algorithm for accurate brain tumor classification. The deep learning model achieved high accuracy, aiding in faster and more precise medical diagnostics.

Keywords:

Deep Learning brain tumors healthcare magnetic resonance imaging neural network pre-trained models tumor classification

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Manual brain tumor diagnosis from MRI is complex and time-consuming.
Accurate classification is vital for effective computer-assisted diagnostics (CAD).
Deep learning (DL) offers automated solutions for medical image analysis.

Purpose of the Study:

To develop and evaluate a novel CNN algorithm for classifying glioma, meningioma, and pituitary brain tumors.
To compare the proposed algorithm's performance against established pre-trained models.
To assess the algorithm's accuracy, precision, recall, and F1-score for brain tumor detection.

Main Methods:

A novel Convolutional Neural Network (CNN) algorithm was designed for brain tumor classification.
The algorithm was trained and tested on benchmarked magnetic resonance imaging (MRI) datasets.
Performance was benchmarked against pre-trained models: VGG16, VGG19, ResNet50, MobileNetV2, and InceptionV3.

Main Results:

The novel CNN algorithm achieved a high classification accuracy of 98.04%.
Precision, recall, and F1-score reached a success rate of 98%.
The algorithm demonstrated strong performance compared to existing models.

Conclusions:

The developed CNN algorithm accurately classifies common brain tumor types (glioma, meningioma, pituitary).
The algorithm exhibits good generalization capability and execution speed.
This tool can assist medical professionals in prompt and accurate brain tumor diagnosis.