Publication Type : Conference Paper
Publisher : 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, Bangalore, India.
Source : 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, Bangalore, India (2018)
Url : https://ieeexplore.ieee.org/abstract/document/8494084
Keywords : Alexnet, cancer, Computed tomography, Computer architecture, computerised tomography, Convolutional neural networks, deep ConvNets, Deep convolutional neural network, deep convolutional neural networks, Feature extraction, image classification, Image segmentation, learning (artificial intelligence), learning methodology, LIDC-IDRI dataset, lung, lung cancer, lung nodule classification, lung tumors, Medical Image Processing, neural nets, nonnodules, raw lung CT images, Task analysis, Transfer learning, tumours
Campus : Amritapuri
School : School of Computing, Department of Computer Science and Engineering, School of Engineering
Center : Computer Vision and Robotics
Department : Computer Science
Verified : No
Year : 2018
Abstract : Lung cancer is a hazardous disease which is the unrestricted growth of abnormal cells that can occur in one or both of the lungs. Lung tumors can be of two types such as benign and malignant. The survival rate of lung cancer depends upon early identification of lung nodules which is a crucial process. We propose a new method for Lung nodule classification from Lung CT images by using deep convolutional neural networks [19]. This method eliminates the need of manual feature extraction which is a feed back of previous works. The network is fed with raw lung CT images from publicly available LIDC-IDRI dataset. Here, the lung images are classified into three classes such as: non-nodules, nodules of size lt;;3 mm and nodules of size gt;= 3 mm. This classification is achieved with the help of AlexNet which is a pre-trained convolutional neural network with the help of transfer learning methodology. This method successfully classified the lung CT images into three classes and achieved 98% accuracy with comparatively less false positive rates.
Cite this Research Publication : H. Sathyan and Vinitha Panicker J, “Lung Nodule Classification Using Deep ConvNets on CT Images”, in 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Bangalore, India, 2018