You will get Brain Tumor Classification using Modified Local Binary Patterns (LBP)
Project details
Automatic classification of brain tumor types is very important for accelerating the treatment process, planning and increasing the patient's survival rate. Today, MR images are used to determine the type of brain tumor. Manual diagnosis of brain tumor type depends on the experience and sensitivity of radiologists. Therefore, researchers have developed many brain tumor classification models to minimize the human factor. In this study, two different feature extraction (nLBP and αLBP) approaches were used to classify the most common brain tumor types; Glioma, Meningioma, and Pituitary brain tumors. nLBP is formed based on the relationship for each pixel around the neighbors. The nLBP method has a d parameter that specifies the distance between consecutive neighbors for comparison. Different patterns are obtained for different d parameter values. The αLBP operator calculates the value of each pixel based on an angle value. The classification process was performed by using different machine learning methods. The highest success rate in brain tumor classification was 95.56% with the nLBPd = 1 feature extraction method and Knn model.
Machine Learning Tools
MATLAB, PythonWhat's included
| Service Tiers |
Starter
$500
|
Standard
$600
|
Advanced
$700
|
|---|---|---|---|
| Delivery Time | 20 days | 20 days | 20 days |
Number of Revisions | 3 | 3 | 3 |
Model Validation/Testing | |||
Model Documentation | - | - | - |
Data Source Connectivity | - | - | - |
Source Code | - | - | - |
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Additional Revision
+$100About Yilmaz
Data Scientist & Machine Learning Engineer | Deep Learning, Statistica
Batman, Turkey - 6:44 am local time
1- Develop Machine Learning models? — YES
2-Design Deep Learning architectures? — YES
3-Perform Time-Series Modeling & Forecasting (ECG, EEG, EMG, Vibration, Sensor Streams)? — YES
4-Apply Signal Processing & Feature Extraction (Time/Frequency/Time-Frequency)? — YES
5-Build Statistical Learning Pipelines (Classification/Regression)? — YES
6-Perform Dimensionality Reduction (PCA, UMAP, t-SNE)? — YES
7-Conduct Exploratory Data Analysis (EDA)? — YES
8-Execute Data Cleaning & Preprocessing (Missing, Noise, Outliers)? — YES
9-Train/Validate/Tune ML models with proper CV strategies? — YES
10-Evaluate Model Performance (ROC, PR, F1, RMSE, MAE, R²)? — YES
11-Build Custom Loss Functions & Training Loops? — YES
12-Engineer Features (Statistical/Domain/Spectral)? — YES
13-Visualize Data & Results (Plots, Heatmaps, Trends, Signals)? — YES
14-Process Multi-Channel Signals (ECG, EEG, EMG, IMU, Accelerometer)? — YES
15-Apply Statistical Testing & Inference (ANOVA, Correlation, Hypothesis Tests)? — YES
16-Optimize GPU Training (Batching, Mixed Precision, Memory)? — YES
17-Implement End-to-End ML Pipelines (Data → Model → Metrics → Reports)? — YES
18-Build Multi-Class & Multi-Label Models? — YES
19-Conduct Research-Grade Experimentation & Benchmarking? — YES
20-Generate Technical Reports, Figures, Tables & Summaries? — YES
21-Provide Technical Consulting & Model Design Guidance? — YES
22-Mentor on ML/DL/Time-Series topics (Academic or Industrial)? — YES
23-Build Computer Vision Models (Image)? — YES
24-Apply Image Preprocessing & Feature Extraction (Filters, Edges, Keypoints)? — YES
25-Perform Object Detection/Tracking (YOLO, Faster-RCNN, SORT, DeepSORT)? — YES
26-Implement Image Classification & Segmentation (CNN/UNet/ViT)? — YES
27-Conduct Text Mining & NLP (Tokenization, Lemmatization, N-grams)? — YES
28-Build Text Classification & Topic Modeling (TF-IDF, LDA, BERT)? — YES
29-Perform Semantic Similarity & Embedding Analysis (Word2Vec, SBERT)? — YES
30-Develop NLP Pipelines for Sentiment/Entity/Keyword Extraction? — YES
31-Integrate LLMs for Knowledge Retrieval & Reasoning (RAG/Vector DB)? — YES
32-Fine-Tune or Customize LLMs (Instruction-Tuning/Domain Adaption)? — YES
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