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alasulu / repository
This repository contains MATLAB preprocessing/segmentation, feature extraction code for the project. For the upcoming part of the project, use the WEKA with extracted features .csv files.
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This repository presents a MATLAB-based workflow for brain tumor detection and analysis using medical image processing techniques.
The project focuses on the core stages of a classical biomedical imaging pipeline:
It is designed as both a technical project repository and a practical study resource for biomedical signal and image processing.
The repository contains MATLAB code for preprocessing and segmentation, along with feature extraction scripts and a project report. The repository description also indicates that the extracted feature .csv files are intended for use in WEKA for the next classification stage.
This makes the project a complete bridge between:
Brain tumor analysis from MRI images is a highly relevant biomedical engineering problem.
This project demonstrates a structured workflow for tumor-focused image analysis by combining:
It is especially valuable for students and engineers interested in:
.
├── Biomedical_Project_Report.pdf # Full report and technical documentation
├── feature_extraction_code.m # Feature extraction from processed/segmented regions
├── mask_transfer_code.m # Mask transfer operations
├── merge_image_code.m # Image merging / combined processing stage
├── segmenter_example.m # Segmentation example workflow
└── tumor_transfer_code.m # Tumor-region transfer / mapping operations
Selected from shared topics, language and repository description—not editorial ratings.
TheAlgorithms /
This repository contains algorithms written in MATLAB/Octave. Developing algorithms in the MATLAB environment empowers you to explore and refine ideas, and enables you test and verify your algorithm.
83/100 healthmilaan9 /
This repository contains the collection of UCI (real-life) datasets and Synthetic (artificial) datasets (with cluster labels and MATLAB files) ready to use with clustering algorithms.
86/100 healthThe project follows a staged biomedical image processing pipeline:
MRI images are processed in MATLAB to isolate relevant regions and prepare the data for tumor analysis.
Mask transfer and tumor-region transfer scripts are used to manipulate and preserve important tumor-related image regions.
Relevant numerical features are extracted from processed images and tumor-related regions.
The repository is designed so that extracted feature tables can be exported and used in WEKA for machine learning-based classification.
The project uses MATLAB as the main implementation platform for image-level operations and algorithm development.
The repository includes a segmentation example and multiple transfer-oriented scripts, suggesting an emphasis on isolating tumor-related structures before feature analysis.
A dedicated feature extraction script is included, indicating that the workflow does not stop at visualization or segmentation, but moves toward quantitative analysis.
The repository description explicitly notes that extracted feature .csv files are intended for WEKA in the next stage, making this project suitable for hybrid MATLAB + ML workflows.
This project is a strong example of how classical image processing can support medical decision workflows.
It demonstrates:
A typical workflow for using this repository would be:
.csvsegmenter_example.mProvides an example segmentation pipeline or initial tumor-region separation process.
mask_transfer_code.mApplies or transfers mask information between image representations or processing stages.
tumor_transfer_code.mTransfers or isolates tumor-specific regions for subsequent analysis.
merge_image_code.mCombines intermediate outputs or integrates processed image layers.
feature_extraction_code.mComputes measurable features for later statistical or machine learning analysis.
This type of workflow can support:
.csvThis repository can be extended with several high-impact upgrades:
This repository is especially useful for learners and researchers interested in:
It can be used as a foundation for:
The repository includes a project report:
Biomedical_Project_Report.pdf
This should be the first reference point for understanding the project background, methodology, and implementation decisions.
Oğuzhan Alasulu
@misc{alasulu_brain_tumor_matlab,
author = {Oğuzhan Alasulu},
title = {Advanced Brain Tumor Detection Algorithm Utilizing MATLAB Project},
year = {2026},
howpublished = {\url{https://github.com/alasulu/Advanced-Brain-Tumor-Detection-Algorithm-Utilizing-MATLAB-Project}}
}
This project reflects a classical but highly instructive biomedical imaging pipeline: process the MRI, isolate the suspicious region, extract meaningful features, and prepare the data for machine learning-driven tumor analysis.
This repository contains MATLAB and Simulink files used in the "How to design motor controllers using Simscape Electrical" videos.
Al-Sad /
This repository contains all MATLAB and Python codes used for Drones Detection and Identification using their RF signals.
76/100 healthmindThomas /
Main repository for the Kugle robot project. The repository contains the MATLAB code and Simulink models for the Kugle robot developed as part of the master thesis work. This includes a non-linear Quaternion ballbot model, Sliding mode attitude controller, Quaternion Extended Kalman filter and ACADO MPC for path-following.
59/100 healthzhixuany /
This repository contains official code (in MATLAB) for exploring and visualizing HUMBI dataset introduced in the paper "HUMBI: A Large Multiview Dataset of Human Body Expressions" (CVPR 2020).
71/100 health