Skin cancer Detection

[ananyag309]

Is your feature request related to a problem? Please describe.
Early detection of skin cancer is crucial but can be challenging with traditional methods, which rely on dermatologist expertise and may not always be accessible or accurate. There is a need for a machine learning-based tool to assist in early diagnosis.

Describe the solution you'd like
Develop a machine learning model to predict skin cancer from dermoscopic images. The model should classify lesions as benign or malignant, providing visual explanations (e.g., heatmaps) to highlight influential areas. A simple interface would allow users to upload an image and receive a prediction.

Describe alternatives you've considered
Traditional Diagnosis: Effective but limited by availability and expertise.
Manual Image Analysis Software: Lacks the predictive power of modern ML models.
Transfer Learning: Using pre-trained models like ResNet or VGG to improve accuracy and speed up training.

Approach to be followed (optional)
Data Preparation: Use datasets like ISIC, perform augmentation, and preprocess images.
Model Training: Train a CNN or fine-tune a pre-trained model (e.g., ResNet50).
Model Evaluation: Optimize using metrics like accuracy and ROC-AUC, with hyperparameter tuning.
Deployment: Provide a web app for predictions, including visual explanations for interpretability.

Additional context
This project could aid dermatologists and improve research in medical imaging while ensuring diverse data representation.

To be Mentioned while taking the issue:

• What is your participant role? -- GSSoC

Note:

• Please review the project documentation and ensure your code aligns with the project structure.
• Please ensure that either the predict.py file includes a properly implemented model_details() function or the notebook contains this function to print a detailed model report. The model will not be accepted without this function in place, as it is essential for generating the necessary model details.
• Prefer using a new branch to resolve the issue, as it helps keep the main branch stable and makes it easier to manage and review your changes.
• Strictly use the pull request template provided in the repository to create a pull request.