Lung Cancer Classification Using Combination Of Efficientnet And Visual Geometry Group Algorithm

Authors

  • Amir Mahmud Husein Prima Indonesia University, Indonesia
  • Rishi Astasachindra Prima Indonesia University, Indonesia
  • Pedro Samuel Sormin Prima Indonesia University, Indonesia
  • Veryl Lovely Prima Indonesia University, Indonesia
  • Atap Gultom Prima Indonesia University, Indonesia

DOI:

10.33395/sinkron.v8i3.13831

Keywords:

Convolutional Neural Network, Classification, EfficientNet-B7, Histopathology, Lung Cancer, VGG-16

Abstract

Lung cancer is one of the leading causes of mortality All around the world. It is classified into three main types: Adenocarcinoma of the lung (ACA), Non-small cell lung cancer (N), and Squamous Cell Carcinoma of the lung (SCC). Lung Cancer Classification is crucial on development of effective treatments. This study aims to improve the accuracy of lung cancer classification through the integration of a hybrid model, which combines two Convolutional Neural Networks architectures, namely EfficientNet-B7 and VGG-16. A set of histopathology images was subjected to testing, with the data split into three categories: 60% for training, 30% for validation, and 10% for testing. Prior to use, each image underwent a preprocessing process, wherein it was resized to 256x256 pixels. The model test results achieved an accuracy, precision, recall, and F1-score of 98.73%, which is superior to the EfficientNet-B7 base model. The findings of this study demonstrate the potential of hybrid models to improve accuracy in lung cancer classification. The utilization of hybrid models has the potential to contribute significantly to the beginning diagnosis and appropriate Lung Cancer Therapies. Future research will focus on improving the model through the application of image segmentation techniques and expanding the scope of classification to other types of lung cancer. Optimization of the hybrid model architecture using novel techniques such as the attention mechanism or transfer learning will be conducted to improve the efficiency and accuracy of the model. Additionally, a system that can be integrated into clinical practice will be developed

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Author Biographies

Amir Mahmud Husein, Prima Indonesia University, Indonesia

received his Master's degree in Computer Science
from Universitas Sumatera Utara (USU) in 2011. He currently serves as a lecturer in
Computer Science at Universitas Prima Indonesia (UNPRI). His research interests
include various areas of big data, computer vision, and data science. In this research,
his main role is in conceptualization, methodology, investigation and resources. He
can be contacted at email: [email protected]

Rishi Astasachindra, Prima Indonesia University, Indonesia

currently a student at Prima Indonesia University
majoring in Computer Science. He is under the guidance of Mr. Amir Mahmud
Husein. His research interests are mainly in the areas of computer vision, data science,
and machine learning. In this research, his main role is in project administration,
conceptualization, methodology, investigation, resources, programming, data
analysis and writing. He can be contacted at email: [email protected]

Pedro Samuel Sormin, Prima Indonesia University, Indonesia

currently a student at Prima Indonesia University
majoring in Computer Science. He is under the guidance of Mr. Amir Mahmud
Husein. His research interests are in the field of computer vision and data science. In
this research his main role is validation and writing. He can be contacted at email:
[email protected]

Veryl Lovely, Prima Indonesia University, Indonesia

currently a student at Prima Indonesia University majoring
in Computer Science. He is under the guidance of Mr. Amir Mahmud Husein. His
research interests are in the field of computer vision and data science. In this research
his main role is validation and visualization. He can be contacted at email:
[email protected]

Atap Gultom, Prima Indonesia University, Indonesia

currently a student at Prima Indonesia University majoring
in Computer Science. He is under the guidance of Mr. Amir Mahmud Husein. His
research interest is in the field of computer vision and data science. In this research,
his main role is in data analysis and writing. He can be contacted at email:
[email protected]

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Husein, A. M., Astasachindra, R., Sormin, P. S., Lovely, V., & Gultom, A. (2024). Lung Cancer Classification Using Combination Of Efficientnet And Visual Geometry Group Algorithm. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(3), 2022-2036. https://doi.org/10.33395/sinkron.v8i3.13831

Issue

Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024

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Articles

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Copyright (c) 2024 Amir Mahmud Husein, Rishi Astasachindra, Pedro Samuel Sormin, Veryl Lovely, Atap Gultom

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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