International Journal of Advance Computational Engineering and Networking (IJACEN)
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Volume-12,Issue-9  ( Sep, 2024 )
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Statistics report
Feb. 2025
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 141
Paper Published : 1672
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  Journal Paper


Paper Title :
Computed Tomography-Based Lung Cancer Detection Using Parallel Deep Convolution Neural Network

Author :Karishma Doke, M S Biradar, B.Shiragapur

Article Citation :Karishma Doke ,M S Biradar ,B.Shiragapur , (2024 ) " Computed Tomography-Based Lung Cancer Detection Using Parallel Deep Convolution Neural Network " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 77-86, Volume-12,Issue-9

Abstract : Globally, lung cancer is the primary cause of death. The Lung Cancer Detection (LCD) is critical in the initial stages because of the asymptomatic nature and complex structure of the lung computed tomography (CT) images. Numerous deep learning (DL)-based plans have emerged in recent years, significantly improved LCD efficiency. However, the effectiveness of the DL-based schemes is limited because of complicated network topology, less generalization capability, poor feature representation, and class imbalance problems. This article presents LCD based on Parallel Convolution Neural Networks (LC-PCNN) to improve the feature distinctiveness and generalization capability. Further, it utilizes data augmentation (DA) based on rotation, scaling, shifting, and noise addition to minimize the problem of class imbalance. The outcomes of the proposed LC-PCNN are estimated on the Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) Lung Cancer Dataset. The LC-PCNN offers an accuracy of 99.14%, recall of 0.99, precision of 0.99, F1-score of 0.99, selectivity of 0.99, also NPV of 0.99 for the augmented dataset, which is superior over traditional LCD methods. Keywords - Biomedical Image Processing, Computed Tomography, Deep Convolution Neural Network, Deep Learning, Data Augmentation

Type : Research paper

Published : Volume-12,Issue-9


DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-21251   View Here

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