International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 186 - Issue 8 |
Published: February 2024 |
Authors: Saleh Abunajm, Nelly Elsayed, Zag Elsayed, Murat Ozer |
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Saleh Abunajm, Nelly Elsayed, Zag Elsayed, Murat Ozer . Deep Learning Approach for Early Stage Lung Cancer Detection. International Journal of Computer Applications. 186, 8 (February 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 }, year = { 2024 }, volume = { 186 }, number = { 8 }, pages = { 11-17 }, doi = { 10.5120/ijca2024923429 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2024 %A Saleh Abunajm %A Nelly Elsayed %A Zag Elsayed %A Murat Ozer %T Deep Learning Approach for Early Stage Lung Cancer Detection%T %J International Journal of Computer Applications %V 186 %N 8 %P 11-17 %R 10.5120/ijca2024923429 %I Foundation of Computer Science (FCS), NY, USA
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.