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license: apache-2.0

Skin Cancer Image Classification Model

Introduction

This model is designed for the classification of skin cancer images into various categories including benign keratosis-like lesions, basal cell carcinoma, actinic keratoses, vascular lesions, melanocytic nevi, melanoma, and dermatofibroma.

Model Overview

  • Model Architecture: Vision Transformer (ViT)
  • Pre-trained Model: Google's ViT with 16x16 patch size and trained on ImageNet21k dataset
  • Modified Classification Head: The classification head has been replaced to adapt the model to the skin cancer classification task.

Dataset

  • Dataset Name: Skin Cancer Dataset
  • Source: Marmal88's Skin Cancer Dataset on Hugging Face
  • Classes: Benign keratosis-like lesions, Basal cell carcinoma, Actinic keratoses, Vascular lesions, Melanocytic nevi, Melanoma, Dermatofibroma

Training

  • Optimizer: Adam optimizer with a learning rate of 1e-4
  • Loss Function: Cross-Entropy Loss
  • Batch Size: 32
  • Number of Epochs: 5

Evaluation Metrics

  • Train Loss: Average loss over the training dataset
  • Train Accuracy: Accuracy over the training dataset
  • Validation Loss: Average loss over the validation dataset
  • Validation Accuracy: Accuracy over the validation dataset

Results

  • Epoch 1/5, Train Loss: 0.7168, Train Accuracy: 0.7586, Val Loss: 0.4994, Val Accuracy: 0.8355
  • Epoch 2/5, Train Loss: 0.4550, Train Accuracy: 0.8466, Val Loss: 0.3237, Val Accuracy: 0.8973
  • Epoch 3/5, Train Loss: 0.2959, Train Accuracy: 0.9028, Val Loss: 0.1790, Val Accuracy: 0.9530
  • Epoch 4/5, Train Loss: 0.1595, Train Accuracy: 0.9482, Val Loss: 0.1498, Val Accuracy: 0.9555
  • Epoch 5/5, Train Loss: 0.1208, Train Accuracy: 0.9614, Val Loss: 0.1000, Val Accuracy: 0.9695

Conclusion

The model demonstrates good performance in classifying skin cancer images into various categories. Further fine-tuning or experimentation may improve performance on this task.