Open AccessEfficient CNN for Lung Cancer Detection
Author(s) -
Venkata Tulasiramu Ponnada,
Dr.S.V.Naga Srinivasu
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b2921.078219
Subject(s) - effi , computer science , convolution (computer science) , layer (electronics) , artificial intelligence , image (mathematics) , filter (signal processing) , pattern recognition (psychology) , algorithm , computer vision , materials science , database , artificial neural network , composite material
The machine learning based solutions for medical image analysis are successful in detection of wide variety of anomalies in imaging procedures. The aim of the medical image analysis systems based on machine learning methods is to improve the accuracy and minimize the detection time. The aim in turn contributes to early disease detection and extending the patient life. This paper presents an efficient CNN (EFFI-CNN) for Lung cancer detection. EFFI-CNN consists of seven CNN layers (i.e. Convolution layer, Max-Pool layer, Convolution layer, Max-Pool layer, fully connected layer, fully connected layer and Soft-Max layer). EFFI-CNN uses lung CT scan images from LIDC-IDRI and Mendeley data sets. EFFI-CNN has a unique combination of CNN layers with parameters (Depth, Height, Width, filter Height and filter width).