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Related Experiment Video

Updated: Aug 27, 2025

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

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

Published on: November 30, 2022

Accurate brain tumor detection using deep convolutional neural network.

Md Saikat Islam Khan¹, Anichur Rahman^1,2, Tanoy Debnath^1,3

¹Department of CSE, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh.

Computational and Structural Biotechnology Journal

|September 23, 2022

Summary

This summary is machine-generated.

This study introduces two deep learning models for accurate brain tumor detection and classification using Magnetic Resonance Imaging (MRI). The models achieve high accuracy, outperforming existing methods in identifying normal vs. abnormal and specific tumor types.

Keywords:

Brain tumor Computer-assisted diagnosis Convolutional neural network Data augmentation Magnetic reasoning imaging

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Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Published on: June 7, 2020

Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

Manual Magnetic Resonance Imaging (MRI) analysis for brain tumor detection is time-consuming and requires specialized expertise.
Advancements in Computer-Assisted Diagnosis (CAD), machine learning, and deep learning offer improved reliability in identifying brain tumors.
Traditional machine learning methods often necessitate handcrafted feature extraction for classification.

Purpose of the Study:

To propose two deep learning models for the binary (normal vs. abnormal) and multiclass (meningioma, glioma, pituitary) classification of brain tumors.
To address the challenge of overfitting in deep learning models when dealing with limited medical imaging datasets.
To compare the performance of the proposed models against existing state-of-the-art methods.

Main Methods:

Development of a 23-layer Convolutional Neural Network (CNN) for a large dataset.
Implementation of transfer learning by combining VGG16 architecture with a modified CNN for a smaller dataset to mitigate overfitting.
Utilized two publicly available MRI datasets comprising 3064 and 152 images.

Main Results:

The proposed models achieved classification accuracies of up to 97.8% and 100% on the respective datasets.
Both models demonstrated superior performance compared to other state-of-the-art models in the literature.
The transfer learning approach effectively handled the overfitting issue in the smaller dataset.

Conclusions:

Deep learning models, particularly with transfer learning, are highly effective for automated brain tumor detection and classification from MRI scans.
The developed models offer a reliable and accurate alternative to manual analysis, improving diagnostic efficiency.
The study provides publicly available models, datasets, and code to facilitate further research and development in the field.