A machine learning project for classifying lung cancer using CT scan data. It employs semi-supervised learning with Label Spreading to improve classification with limited labeled data, differentiating between malignant and benign lung nodules for early detection.

Lung Cancer Classification using Computerized Tomography (CT) Data

Assignment for Laboratory IA and CD course, 3rd Year, 1st Semester, Bachelor in Artificial Intelligence and Data Science.

Laboratory of Artificial Intelligence and Data Science Class, 3º Year, 1º Semester. Project 1 - Lung Cancer Classification using Computerized Tomography (CT) Data

Laboratory of Artificial Intelligence and Data Science Class, 3º Year, 1º Semester. Project 1 - Lung Cancer Classification using Computerized Tomography (CT) Data

Lung Cancer Classification using Computerized Tomography (CT) Data

*Lung Cancer Classification using Computerized Tomography (CT) Data*

This project develops a Data Science solution for lung cancer classification using CT scans from the LIDC-IDRI dataset. It explores radiomics and machine learning techniques to assess lung nodule malignancy, supporting early detection and improving clinical decision-making through automated analysis.

A full-cycle medical imaging pipeline that extracts and analyzes 2D and 3D radiomic features from CT scans to classify lung nodules. Utilizing the LIDC-IDRI dataset, this project benchmarks multiple feature selection techniques (Lasso, PCA, t-test) and ensemble classifiers (XGBoost, Random Forest) to optimize diagnostic sensitivity.

Laboratory of Artificial Intelligence and Data Science Class, 3º Year, 1º Semester. Project 1 - Lung Cancer Classification using Computerized Tomography (CT) Data