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Breast Cancer Detection Model #163
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Congratulations, @Taranpreet10451! 🎉 Thank you for creating your issue. Your contribution is greatly appreciated and we look forward to working with you to resolve the issue. Keep up the great work! We will promptly review your changes and offer feedback. Keep up the excellent work! Kindly remember to check our contributing guidelines |
@SrijanShovit Please assign me this issue I want to work on it. |
@sanjay-kv Sir I can use deep learning in this. |
Sir I will be using Deep Learning so I think it should be of level 2. |
no what ever contribution to this repo, will be level 1 , this is intended for beginners to learn . unless its technical and deployment level no higher level. sorry about that. you can explore my other repo for level 2 level3 issues. |
Sir then shall I use Deep Learning? |
upto you if you making a folder library level 1 be the max in this repo. |
Sir I have created a Pull Request please review it. |
This issue has been automatically closed because it has been inactive for more than 7 days. If you believe this is still relevant, feel free to reopen it or create a new one. Thank you! |
Is your feature request related to a problem? Please describe.
Breast cancer is a leading cause of cancer-related deaths among women worldwide. Early detection and accurate diagnosis are crucial for improving survival rates and outcomes for patients. However, current diagnostic methods, including mammograms and biopsies, can be time-consuming, expensive, and sometimes yield false positives or negatives. There is a need for a more efficient, accurate, and accessible method for breast cancer detection.
Describe the solution you'd like along with reference dataset.
Key Features:
Data Collection and Preparation: Collect and preprocess a dataset of breast cancer diagnostic data, including features such as tumor size, cell shape, and other clinical attributes.
Feature Selection: Identify and select the most relevant features for the model.
Machine Learning Model: Develop a basic machine learning model (e.g., logistic regression, decision tree) to classify the data as benign or malignant.
Model Evaluation and Validation: Evaluate the model's performance using appropriate metrics and validate its effectiveness on unseen data.
Describe alternatives you've considered
No response
Additional context
No response
Code of Conduct
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