Paper Info
Title
Light Deep Model for Pulmonary Nodule Detection from CT Scan Images for Mobile Devices
Abstract
The emergence of cognitive computing and big data analytics revolutionize the healthcare domain, more specifically in detecting cancer. Lung cancer is one of the major reasons for death worldwide. The pulmonary nodules in the lung can be cancerous after development. Early detection of the pulmonary nodules can lead to early treatment and a significant reduction of death. In this paper, we proposed an end-to-end convolutional neural network- (CNN-) based automatic pulmonary nodule detection and classification system. The proposed CNN architecture has only four convolutional layers and is, therefore, light in nature. Each convolutional layer consists of two consecutive convolutional blocks, a connector convolutional block, nonlinear activation functions after each block, and a pooling block. The experiments are carried out using the Lung Image Database Consortium (LIDC) database. From the LIDC database, 1279 sample images are selected of which 569 are noncancerous, 278 are benign, and the rest are malignant. The proposed system achieved 97.9% accuracy. Compared to other famous CNN architecture, the proposed architecture has much lesser flops and parameters and is thereby suitable for real-time medical image analysis.
Year
DOI
Venue
2020
10.1155/2020/8893494
WIRELESS COMMUNICATIONS & MOBILE COMPUTING
DocType
Volume
ISSN
Journal
2020.0
1530-8669
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Mehedi Masud17726.95
Ghulam Muhammad210615.27
Mohammod Shamim Hossain326834.68
Hesham Alhumyani463.36
Sultan S. Alshamrani573.89
Omar Cheikhrouhou66611.71
S. Ibrahim7295.07