Machine Learning for Early Cancer Prediction

Team Members:

• Praveen V
• Kabileshwaran SD

1. Introduction:

In contemporary healthcare, early cancer detection is of paramount importance. Identifying cancerous conditions at an early stage significantly improves patient outcomes and enables more effective treatment strategies. This project aims to leverage the potential of machine learning and deep learning methodologies to predict three prominent types of cancers: oral, cervical, and brain tumors.

2. Rationale:

The project is motivated by the transformative capabilities of machine learning in healthcare. Early detection empowers medical professionals to initiate interventions at the earliest stages of cancer, increasing the likelihood of successful treatment and improving overall patient prognosis. By employing advanced computational techniques, we intend to develop a sophisticated predictive model capable of analyzing medical images and risk factor data to identify potential cases of oral, cervical, and brain cancers in their early stages.

3. Objectives:

• Develop and fine-tune machine learning models for the early detection of oral, cervical, and brain tumors.
• Utilize comprehensive datasets, comprising both image and risk factor data, to train and validate the predictive models.
• Evaluate the performance of the models using metrics such as accuracy, sensitivity, and specificity.
• Establish a foundation for the seamless integration of predictive models into clinical practice, enhancing diagnostic capabilities in cancer detection.

GitHub Repository Readme:

Project Structure:

• /Code: Contains the source code for machine learning models.
• /Dataset: Holds datasets used for training and testing the models.
• /Models: Storing models for future use.

Installation:

1. Clone the repository: git clone https://github.com/Praveenanand333/Early-Cancer-Prediction.git
2. Install required dependencies: pip install -r requirements.txt

Dataset:

• The dataset used for training and testing the models is available in the /Dataset directory.The dataset is obtained from kaggle.

Contributing:

• We welcome contributions and collaboration. Fork the repository, create a new branch, and submit a pull request.

Issues:

• If you encounter any issues or have suggestions, please open an issue on the GitHub repository.

License:

• This project is licensed under the MIT License.