Deep 3D Convolutional Neural Network for Automated Lung Cancer Diagnosis

Autor:	Mishra, Sumita, Chaudhary, Naresh Kumar, Asthana, Pallavi, Kumar, Anil
Rok vydání:	2019
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
DOI:	10.1007/978-981-13-7150-9_16
Popis:	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. Comment: Initial draft of PAPER Presented at IRSCNS 2018 , Goa , India final version available at Mishra S., Chaudhary N.K., Asthana P., Kumar A. (2019) Deep 3D Convolutional Neural Network for Automated Lung Cancer Diagnosis. In: Peng SL., Dey N., Bundele M. (eds) Computing and Network Sustainability. Lecture Notes in Networks and Systems, vol 75. Springer, Singapore
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1906.01054 Zobrazit plný text záznamu View this record from Arxiv