UppuluriKalyani/ML-Nexus

Skin Cancer Detection

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ananyag309 commented

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.

github-actions commented

Thanks for creating the issue in ML-Nexus!🎉
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github-actions commented

Thanks for raising this issue! However, we believe a similar issue already exists. Kindly go through all the open issues and ask to be assigned to that issue.