Lung cancer prediction using convolutional neural network based VGG19 compared with VGG16 using CT-scan images for accuracy improvement

The main aim of this work is to calculate the accuracy in early prediction of lung cancer using CT images.: The dataset of 989 lung images has been taken from the Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases with 80% of G-power. The proposed method contains image preprocessing...

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Bibliographic Details
Main Authors: Nali, Mohana Krishna, Ramalingam, Puviarasi
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Artificial neural networks
Computed tomography
Datasets
Medical imaging
Model accuracy
Software
Online Access:Get full text
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Summary:The main aim of this work is to calculate the accuracy in early prediction of lung cancer using CT images.: The dataset of 989 lung images has been taken from the Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases with 80% of G-power. The proposed method contains image preprocessing, augmentation and features extraction of images using Convolution Neural Network (CNN) based VGG16 and VGG19 models. Based on training (80%), validation (18%) and testing (2%) of the dataset in python software the accuracy and precision is calculated. A comparative analysis is made between two algorithms using SPSS software. Results : The proposed model CNN-VGG19 produced improved accuracy of 0.9599±0.0217 than VGG 16 with the significance value of
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0119177