Classification of MRI Scans using Radiomics and MLP

This article explores the application of radiomics and multi-layer perceptrons (MLPs) for improved brain tumor detection and classification using MRI scans. Radiomics extracts quantitative features (shape, texture, statistical properties) from regions of interest within the MRI images, providing a richer dataset for analysis than visual inspection alone. These features are then used to train an MLP, a type of neural network, to classify scans as containing a tumor ("yes") or not ("no").

Key Learning Points:

• Handcrafted Feature Extraction with Radiomics: The article details the process of extracting radiomic features, emphasizing their role in capturing complex tumor characteristics not readily apparent in visual analysis.
• MRI Scan Analysis Enhancement: Radiomics significantly improves the speed and accuracy of tumor detection and classification from MRI scans.
• Multi-Class Classification: The extracted features are utilized to classify brain scans into distinct categories (in this case, tumor present or absent).
• MLP for Classification: The article demonstrates the use of an MLP for robust classification based on the extracted radiomic features.

Methodology Overview:

The study utilizes a brain tumor dataset from Kaggle. The process involves:

1. Data Preparation: Loading images and creating binary masks to define the region of interest (ROI) for feature extraction.
2. Feature Extraction: Employing the PyRadiomics library to extract a wide range of radiomic features from the masked ROIs.
3. Data Preprocessing: Cleaning and standardizing the extracted features, handling missing values, and preparing the data for the MLP. This includes converting categorical labels ("yes"/"no") into numerical representations (1/0).
4. MLP Model Training: Building and training a two-hidden-layer MLP using PyTorch. The model is trained using the Adam optimizer and the cross-entropy loss function. Dropout regularization is applied to prevent overfitting.
5. Model Evaluation: Assessing the trained MLP's performance on a held-out test set using accuracy as the evaluation metric. A loss curve is plotted to visualize the training process.

Results and Conclusion:

The trained MLP achieves a high accuracy (94.50%) on the test dataset, demonstrating the effectiveness of the combined radiomics and MLP approach for brain tumor classification. The article concludes that this method offers a significant improvement in diagnostic efficiency and accuracy, assisting healthcare professionals in making faster and more informed decisions.

(Note: The images are included as requested, maintaining their original format and position. The code snippets are omitted for brevity, but the core steps and results are summarized.)

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