Skin Cancer Classification Project Guide

Skin cancer is among the most common types of cancer worldwide, and early detection plays a crucial role in improving survival rates. However, manual diagnosis by dermatologists can be challenging due to the subtle differences between benign and malignant lesions. Deep learning models trained on dermoscopic images can assist medical professionals by accurately classifying skin lesions, enabling faster diagnosis, reducing workload, and reaching patients in remote areas.

Convolutional Neural Networks (CNNs) can learn subtle features from dermoscopic images, such as asymmetry, border irregularities, color distribution, and patterns that distinguish benign from malignant lesions. Transfer learning with models like ResNet, EfficientNet, or InceptionV3 improves accuracy significantly. Data augmentation, lesion segmentation, and ensemble learning can further boost performance, making your model reliable and suitable for healthcare deployments.

The system receives a dermoscopic image, preprocesses it (resizing, normalization, hair removal if needed), and feeds it into a CNN model trained to classify skin lesions. Data augmentation techniques simulate variations in imaging conditions. If the model predicts a high probability of malignancy, users are advised to seek immediate medical consultation. With continuous training on diverse datasets, the system improves robustness across skin tones, lighting, and camera types.

• Collect datasets like ISIC Archive containing labeled dermoscopic images for different types of skin lesions.
• Preprocess: resize images, normalize color channels, and perform lesion segmentation if needed.
• Train CNN models such as ResNet, EfficientNet, or custom architectures for binary (benign/malignant) or multi-class classification.
• Evaluate using metrics like AUC-ROC, sensitivity, specificity, and balanced accuracy to ensure medical-grade reliability.
• Deploy the system through a web app or mobile app enabling patients and doctors to easily upload and analyze skin images.

• ISIC Skin Lesion Dataset
• TensorFlow Image Classification Tutorials
• Skin Lesion Segmentation with Keras