Citation

Chai, Chee Chiet and Khoh, Wee How and Pang, Ying Han and Yap, Hui Yen (2024) A Lung Cancer Detection with Pre-Trained CNN Models. Journal of Informatics and Web Engineering, 3 (1). pp. 41-54. ISSN 2821-370X

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Abstract

Lung cancer is a common cancer in Malaysia, affecting the majority of male citizens. The early detection of lung cancer will decrease its death rate. The only way to detect lung cancer is with a CT scan, and it also requires the doctor to check the scan to confirm the disease. In another way, the computer's support for the detection and diagnosis tool will assist doctors in determining lung cancer more accurately and efficiently. There are three main objectives for this research work. The first target is to study state-of-the-art research work to detect and recognize lung cancer from CT scan images. Then, the article will aim to adopt pre-trained convolutional neural network models in lung cancer detection. It also evaluates the performance of convolutional models on lung cancer imagery data. Then, the pre-trained models with a few added layers and modifications to parameters such as epochs, batch size, optimizer, etc. to conduct model training in this article. After that, Python Pylidc is used in image pre-processing to filter the dataset. Overall, pre-trained models such as ResNet-50, VGG-16, Xception, and MobileNet achieve above-state-of-the-art performance in classifying lung cancer from CT scan images in the range of 78% to 86% accuracy. The best detection accuracy result is the pre-trained VGG-16 model with the addition of some fully connected layers, 16 batch sizes, and the Adam optimizer, which achieved 86.71%.

Item Type:	Article
Uncontrolled Keywords:	Convolution neural network, Lung Cancer, Image Processing, pre-trained CNN models
Subjects:	R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Institute for Postgraduate Studies (IPS)
Depositing User:	Mr. MUHAMMAD AZRUL MOSRI
Date Deposited:	02 Apr 2024 00:03
Last Modified:	02 Apr 2024 00:03
URII:	http://shdl.mmu.edu.my/id/eprint/12241

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