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Deep Learning Approach for Early Stage Lung Cancer Detection

by Saleh Abunajm, Nelly Elsayed, Zag Elsayed, Murat Ozer
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 8
Year of Publication: 2024
Authors: Saleh Abunajm, Nelly Elsayed, Zag Elsayed, Murat Ozer
10.5120/ijca2024923429

Saleh Abunajm, Nelly Elsayed, Zag Elsayed, Murat Ozer . Deep Learning Approach for Early Stage Lung Cancer Detection. International Journal of Computer Applications. 186, 8 ( Feb 2024), 11-17. DOI=10.5120/ijca2024923429

@article{ 10.5120/ijca2024923429,
author = { Saleh Abunajm, Nelly Elsayed, Zag Elsayed, Murat Ozer },
title = { Deep Learning Approach for Early Stage Lung Cancer Detection },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2024 },
volume = { 186 },
number = { 8 },
month = { Feb },
year = { 2024 },
issn = { 0975-8887 },
pages = { 11-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number8/deep-learning-approach-for-early-stage-lung-cancer-detection/ },
doi = { 10.5120/ijca2024923429 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-29T03:28:31+05:30
%A Saleh Abunajm
%A Nelly Elsayed
%A Zag Elsayed
%A Murat Ozer
%T Deep Learning Approach for Early Stage Lung Cancer Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 8
%P 11-17
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Lung cancer is the leading cause of death among different types of cancers. Every year, the lives lost due to lung cancer exceed those lost to pancreatic, breast, and prostate cancer combined. The survival rate for lung cancer patients is very low compared to other cancer patients due to late diagnostics. Thus, early lung cancer diagnostics is crucial for patients to receive early treatments, increasing the survival rate or even becoming cancer-free. This paper proposed a deep-learning model for early lung cancer prediction and diagnosis from Computed Tomography (CT) scans. The proposed mode achieves high accuracy while considering low implementation budget. In addition, it can be a beneficial tool to support radiologists’ decisions in predicting and detecting lung cancer and its stage.

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Index Terms

Computer Science
Information Sciences

Keywords

Transfer learning image classification CT scan deep learning

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