# Deep 3D Convolutional Neural Network for Automated Lung Cancer Diagnosis

## TL;DR

This paper introduces a deep 3D CNN for automated lung cancer detection in CT scans, leveraging 3D spatial features for improved accuracy over traditional 2D methods.

## Contribution

It presents a novel end-to-end 3D CNN architecture that automatically learns discriminative 3D features for lung nodule classification.

## Key findings

- Effective classification of lung nodules demonstrated
- Outperforms traditional 2D feature-based methods
- Operates efficiently with limited computational resources

## Abstract

Computer Aided Diagnosis has emerged as an indispensible technique for validating the opinion of radiologists in CT interpretation. This paper presents a deep 3D Convolutional Neural Network (CNN) architecture for automated CT scan-based lung cancer detection system. It utilizes three dimensional spatial information to learn highly discriminative 3 dimensional features instead of 2D features like texture or geometric shape whick need to be generated manually. The proposed deep learning method automatically extracts the 3D features on the basis of spatio-temporal statistics.The developed model is end-to-end and is able to predict malignancy of each voxel for given input scan. Simulation results demonstrate the effectiveness of proposed 3D CNN network for classification of lung nodule in-spite of limited computational capabilities.