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license: mit |
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# Brain Cancer Dataset for Random Contrast Learning (RCL) Classification |
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## Dataset Overview |
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The **Brain Cancer Dataset** is designed for classification tasks using Lumina AI's Random Contrast Learning (RCL) algorithm. This dataset contains features related to brain cancer diagnosis or treatment, such as medical imaging features, patient data, or clinical reports, and aims to classify brain cancer types, stages, or survival outcomes. |
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### Dataset Structure: |
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- **Training Data:** Labeled samples used for training the classification model. |
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- **Testing Data:** Labeled samples used to evaluate the trained model. |
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- **Features:** Patient data, medical imaging features, or relevant medical records. |
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- **Labels:** Brain cancer classification labels such as benign/malignant, cancer types, or survival predictions. |
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## Example Training Command (not pre-split; optimal parameters) |
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```bash |
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C:\PrismRCL\PrismRCL.exe fractal imaginary rclticks=45 boxdown=4 channelpick=4 data=C:\path\to\Brain-Tumor-MRI-2c-Aug-all testsize=0.1 savemodel=C:\path\to\models\mymodel.classify log=C:\path\to\log_files stopwhendone |
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``` |
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### Explanation: |
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- **C:\PrismRCL\PrismRCL.exe:** Path to the PrismRCL executable for classification |
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- **fractal imaginary:** Specifies the fractal imaginary method for training evaluation |
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- **rclticks=45:** Sets the number of RCL iterations during training to 45 |
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- **boxdown=4:** Configuration parameter for training behavior |
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- **channelpick=4:** RCL training parameter |
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- **data=C:\path\to\Brain-Tumor-MRI-2c-Aug-all:** Path to the complete dataset for Brain Tumor MRI classification |
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- **testsize=0.1:** Specifies that 10% of the data should be used for testing |
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- **savemodel=C:\path\to\models\mymodel.classify:** Path to save the resulting trained model |
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- **log=C:\path\to\log_files:** Directory path for storing log files of the training process |
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- **stopwhendone:** Instructs PrismRCL to end the session once training is complete |
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## Usage Instructions: |
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1. **Download the Dataset:** Ensure the datasets are correctly placed in the specified paths. |
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2. **Run the Training Command:** Use the command above to train the classification model. |
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3. **Evaluate the Model:** After training, the model will be saved and ready for testing. |
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4. **Logs:** Check log files for details about the training process. |
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## Original Source |
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This dataset was originally sourced from: |
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1. Figshare: [Brain Tumor Dataset](https://figshare.com/articles/dataset/brain_tumor_dataset/1512427) |
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It was later made available on Kaggle: |
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2. Kaggle: [Brain Tumor MRI Dataset](https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset) |
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Please cite these sources if you use this dataset in your research or applications. |
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## Dataset License: |
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This dataset is available under the MIT License. |
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