LesNet Model 3.1

Model 3.1 employs a deep learning architecture based on InvceptionV3 but tailored for skin lesion classification.
To learn more, Visit the model section of the wiki

Performance

• Recall: 80.27%
• Precision: 93.35%
• Accuracy: 85.40%
• Loss: 0.5113

Model 3 Targets

Metric	Target Range	Progress
Loss	Close to 0
Accuracy	85% - 95%
Precision	80% - 90%
Recall	85% - 95%

SkinVestigatorAI Model V0.1.5 Release Notes

This release brings numerous updates and improvements to the SkinVestigatorAI model, along with a variety of bug fixes.

Major Changes:

1. Model Architecture: The model has been updated to be transformer-based, providing a robust and efficient architecture for sequence understanding and transduction models.
2. Performance Metrics: Added various metrics including F1-score, Specificity, and AUC-ROC to the model evaluation process, providing a comprehensive view of the model's performance.
3. Training Parameters: The training parameters were updated for better optimization and quicker training.
4. Quantization: The model now supports quantization, enhancing model efficiency without significant loss in model performance.

Improvements:

1. The GPU memory usage has been optimized by reducing the batch size and image size, and implementing mixed precision training.
2. The data scraper has been optimized to balance the data before training.
3. The predict view has been improved to automatically select the latest model available in the models folder, and to automatically load in the TFLite model if the system is resource-limited.

Bug Fixes:

1. Fixed a bug that was causing the test data to not load correctly.
2. Fixed a bug related to the model parameters.
3. Fixed a bug that occurred when loading the model with custom metrics.

Other Changes:

1. Adjusted the hyperparameters to mitigate overfitting and enhance the learning rate adaptation. A cooldown period was also introduced to the ReduceLROnPlateau callback for more stable training.
2. Replaced custom precision and recall metrics with TensorFlow's Precision and Recall, while keeping F1 score and specificity as custom metrics due to the absence of direct TensorFlow functions.
3. Refactored the SkinCancerDetector class for improved readability and modularity, and to fix a TensorFlow variable error.
4. Added a print of the model summary on the load of the predict view.
5. Updated the README.md to include the latest model information.

Model Performance:

Test Accuracy: 84.04%
Test Loss: 0.23201
Test Sensitivity: 84.04%
Test Precision: 84.04%
Test F1-score: 84.47%
Test Specificity: 84.02%
Test AUC-ROC: 91.69%

Thank you for your continued support. We look forward to your feedback on this release.

SkinVestigator Nano Model 0.0.3

Changelog

[Version 0.0.3] - Initial Release

• Initial release of the SkinVestigatorAI project.
• Data scraper, model training, and web application.
• Features the "Nano Model" for skin cancer detection.

Nano Model Details

• File size: 40 MB
• Trained on approximately 65000 images
• Quick training time

Here are the results from training the model:

• Training Epochs: 30
• Training steps per epoch: 1463
• Training loss: 0.2246
• Training accuracy: 46.41%
• Validation loss: 0.2526
• Validation accuracy: 46.15%
• Test dataset size: 6685 images
• Test loss: 0.2280
• Test accuracy: 46.38%

The model achieves a high accuracy of 91.38% on the test set. However, it's important to note that there is still some loss present in the model, which could potentially be improved upon in future iterations.

In future updates, the model's performance may be improved by incorporating additional training data, refining the model architecture, and optimizing hyperparameters. This would help enhance the overall efficiency and effectiveness of the skin cancer detection system.