simpleNet: Skin Lesion Classification (Benign vs Malignant)

simpleNet is a lightweight Convolutional Neural Network (CNN) model for binary skin lesion classification, distinguishing between benign and malignant (melanoma) cases.

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.

The model is optimized for generalization and has been validated with external images, showing robust performance for melanoma detection.

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.