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Artificial Intelligence -based Computer Aided Early Diagnosis for low dose CT scans

Principal Investigator
Name
Jolu Ninan
Degrees
Masters Student
Institution
Abu Dhabi University
Position Title
Graduate Researcher
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-450
Initial CDAS Request Approval
Nov 19, 2018
Title
Artificial Intelligence -based Computer Aided Early Diagnosis for low dose CT scans
Summary
The aim of this project is to develop an AI-based computer aided early detection system to help diagnose lung cancer at its early stages. A deep neural net approach will be adopted to classify benign and malignant nodules. Semantic Segmentation will also be used to label anatomy and pathological ROI.
Aims

- Lung Nodule feature recognition-based analysis.
- Classification model to classify benign and malignant lung nodules and management of false positives.
- Risk and survival rate prediction model for lung cancer for the subject in the future.
- Deep Learning approach using neural nets for semantic segmentation of the chest CT of the subject

Collaborators

Jolu Jose Ninan (1039789@students.adu.ac.ae)