Anwarkh1/Skin_Cancer-Image_Classification

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.