multi-cancer detection ml system

machine learning pipeline for lung, breast, and brain cancer detection across tabular and imaging data.

python, tensorflow, scikit-learn, pandas

Managed a 4-person team to build detection workflows for multiple cancer types using clinical and image datasets, engineered and benchmarked 5+ models including CNN, SVM, Naive Bayes, Logistic Regression, and MLP while sustaining 90%+ average accuracy, and implemented feature engineering plus image preprocessing pipelines for normalization and augmentation. Validated model quality with cross-validation, confusion matrices, and ROC analysis, then documented findings for cross-functional review and iteration.