Deep 3D Convolutional Neural Network for Automated Lung Cancer Diagnosis
This addresses the problem of improving accuracy and efficiency in lung cancer diagnosis for radiologists, though it appears incremental as it builds on existing deep learning methods for medical imaging.
The paper tackles automated lung cancer diagnosis from CT scans by proposing a deep 3D CNN that learns discriminative 3D features directly, eliminating the need for manual 2D feature extraction, and it demonstrates effectiveness in classifying lung nodules despite limited computational resources.
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.