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| license: mit |
| tags: |
| - medical |
| - cnn |
| - dermatology |
| - melanoma |
| --- |
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| # simpleNet: Skin Lesion Classification (Benign vs Malignant) |
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| **simpleNet** is a lightweight Convolutional Neural Network (CNN) model for binary skin lesion classification, distinguishing between **benign** and **malignant (melanoma)** cases. |
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| ## Model Description |
| - Architecture: Custom CNN with 3 convolutional blocks and regularization (Batch Normalization, Dropout, L2). |
| - Input size: **224 × 224 × 3 RGB images**. |
| - Output: 2 classes → `["benign", "malignant"]`. |
| - Trained on a combination of curated skin lesion datasets, including **ISIC** samples. |
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| The model is optimized for **generalization** and has been validated with external images, showing robust performance for melanoma detection. |
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| ## Intended Use |
| - **Educational and research purposes**. |
| - Demonstrating the potential of CNNs for medical imaging. |
| - Not intended for clinical use without further validation and regulatory approval. |
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