Cancer Prediction
The "Cancer Prediction" project, conducted under university auspices as part of a master's program, involves the use of Python to analyze medical datasets for predicting cancer through machine learning models.
"Cancer Prediction System" is a robust AI-driven platform developed using Python and machine learning algorithms for analyzing medical datasets to predict cancer. Initiated as part of a master's degree program and in collaboration with two other team members, this project leverages the BreastMNIST and BloodMNIST datasets to train models that predict cancer presence with high accuracy.
Key Features:
Developed in Python: Utilized Python as the primary programming language for creating efficient machine learning models.
Machine Learning Algorithms: Employed various algorithms such as convolutional neural networks (CNNs) for analyzing medical imaging data.
Data Analysis: Conducted comprehensive data preprocessing and augmentation to ensure high-quality datasets for model training.
Collaborative Research: Worked in a team to integrate insights from academic literature and latest research in developing cutting-edge prediction models.
Model Testing and Validation: Rigorous testing and validation processes were implemented to refine the models and enhance their predictive accuracy.
Ethical AI Practices: Ensured that all models adhered to ethical AI practices, including bias mitigation and data privacy concerns.
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Technologies Used:
Python: Utilized for all programming needs, from data preprocessing to model training and evaluation.
Machine Learning Models: Developed to analyze and predict cancer from histopathological and hematological data.
MINST Datasets: Specifically, BreastMNIST and BloodMNIST datasets were used to train and validate the predictive models, providing a foundation for algorithm testing and refinement.
Git Link
