Classification of Lung Cancer with Convolutional Neural Network Method Using ResNet Architecture

Aldrich Deril Christian Zebua; Dedy Yehezkiel Marbun; Felix Thedora; Mawaddah Harahap

doi:10.34148/teknika.v13i2.906

Aldrich Deril Christian Zebua Informatics Engineering Department, Universitas Prima Indonesia, Medan, North Sumatra, Indonesia
Dedy Yehezkiel Marbun Informatics Engineering Department, Universitas Prima Indonesia, Medan, North Sumatra, Indonesia
Felix Thedora Informatics Engineering Department, Universitas Prima Indonesia, Medan, North Sumatra, Indonesia
Mawaddah Harahap Informatics Engineering Department, Universitas Prima Indonesia, Medan, North Sumatra, Indonesia

DOI: https://doi.org/10.34148/teknika.v13i2.906

Keywords: Computer Vision, Convolutional Neural Network, ResNet, Image Classification, Lung Cancer

Abstract

Lung cancer has become one of the most frightening specters in the world of health, leading many people to death each year. Therefore, the classification of lung cancer types is very important to determine the appropriate treatment steps. Considering that lung cancer treatment in the early stages is far more effective and efficient, accurate classification is the key to improving survival rates. This research focuses on the classification of three common lung cancer types: Adenocarcinoma, Large Cell Carcinoma, and Squamous Cell Carcinoma. To achieve optimal results, this study utilizes the ResNet architecture, a deep neural network model that has demonstrated its capabilities in various fields. Before being used on the model, the dataset containing lung X-ray images of patients undergoes preprocessing. At this stage, each image is resized to 256x256 pixels to ensure uniformity and compatibility with the model. Furthermore, this research trains various ResNet models, ranging from ResNet50, ResNet101, to ResNet152, which is the model with the most parameters. By comparing the performance of each model, this study finds that all trained ResNet models are capable of producing good accuracy in classifying lung cancer types. Among these models, ResNet152 demonstrates the most superior performance with an accuracy of 89%. This result suggests that the ResNet architecture has great potential to be used as an aid in classifying lung cancer types with a high level of accuracy. This research makes a significant contribution to the effort to improve the diagnosis and treatment of lung cancer, paving the way for a brighter future for lung cancer patients.

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