Non-Small Cell Lung Cancer (NSCLC) Classification Using Convolutional Neural Network (CNN)

Bunga Mastiti Darmawan; Rizki Wulan Agustin; Farah Noviandini

doi:10.1063/5.0103184

Non-Small Cell Lung Cancer (NSCLC) Classification Using Convolutional Neural Network (CNN)

Bunga Mastiti Darmawan
, Rizki Wulan Agustin
, Farah Noviandini
, Endarko^*

^*Corresponding author for this work

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Small Cell Lung Cancer (SCLC) and Non-Small Cell Lung Cancer (NSCLC) are types of Lung cancer. SCLC usually occurs in patients who have a history of heavy smoking and spreads more rapidly than NSCLC. However, about 80-85% of all lung cancer cases are NSCLC types that mostly attack men and women. This study aimed to classify NSCLC into squamous cell carcinoma, adenocarcinoma, large cell carcinoma, and normal lung and to compare the architecture VGG19 and ResNet50 for classification NSCLC. 1000 images data for each class was used in this study from CT Scan images. Three processes were used for classification processes, such as preprocessing, classification, and validation. The resizing and grayscale process was conducted for preprocessing step to ensure all input images are uniform. The significant result was achieved in the ResNet50 architecture, with an accuracy of 98.35% on testing data, 99.87% on training data, and 96% on validation data. Meanwhile, the best performance on the validation data in the normal class was that the results of precision, sensitivity, F1-score, and specificity were 100%, 100%, 100%, and 100%, respectively.

Original language	English
Title of host publication	4th International Conference of Science and Education Science, IConSSE 2021
Subtitle of host publication	Integrating Rapid Technology and Whole Person Education in Science and Science Education to Encounter the New Normal Era
Editors	Didit Budi Nugroho, Andreas Setiawan, Nur Aji Wibowo, Cucun Alep Riyanto, November Rianto Aminu
Publisher	American Institute of Physics Inc.
ISBN (Electronic)	9780735442597
DOIs	https://doi.org/10.1063/5.0103184
Publication status	Published - 10 Nov 2022
Event	4th International Conference of Science and Education Science: Integrating Rapid Technology and Whole Person Education in Science and Science Education to Encounter the New Normal Era, IConSSE 2021 - Salatiga, Virtual, Indonesia Duration: 7 Sept 2021 → 8 Sept 2021

Publication series

Name	AIP Conference Proceedings
Volume	2542
ISSN (Print)	0094-243X
ISSN (Electronic)	1551-7616

Conference

Conference	4th International Conference of Science and Education Science: Integrating Rapid Technology and Whole Person Education in Science and Science Education to Encounter the New Normal Era, IConSSE 2021
Country/Territory	Indonesia
City	Salatiga, Virtual
Period	7/09/21 → 8/09/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.1063/5.0103184

Cite this

Darmawan, B. M., Agustin, R. W., Noviandini, F., & Endarko (2022). Non-Small Cell Lung Cancer (NSCLC) Classification Using Convolutional Neural Network (CNN). In D. B. Nugroho, A. Setiawan, N. A. Wibowo, C. A. Riyanto, & N. R. Aminu (Eds.), 4th International Conference of Science and Education Science, IConSSE 2021: Integrating Rapid Technology and Whole Person Education in Science and Science Education to Encounter the New Normal Era Article 050004 (AIP Conference Proceedings; Vol. 2542). American Institute of Physics Inc.. https://doi.org/10.1063/5.0103184

Darmawan, Bunga Mastiti ; Agustin, Rizki Wulan ; Noviandini, Farah et al. / Non-Small Cell Lung Cancer (NSCLC) Classification Using Convolutional Neural Network (CNN). 4th International Conference of Science and Education Science, IConSSE 2021: Integrating Rapid Technology and Whole Person Education in Science and Science Education to Encounter the New Normal Era. editor / Didit Budi Nugroho ; Andreas Setiawan ; Nur Aji Wibowo ; Cucun Alep Riyanto ; November Rianto Aminu. American Institute of Physics Inc., 2022. (AIP Conference Proceedings).

@inproceedings{2df880ced54d4ab28b3890eef32d5239,

title = "Non-Small Cell Lung Cancer (NSCLC) Classification Using Convolutional Neural Network (CNN)",

abstract = "Small Cell Lung Cancer (SCLC) and Non-Small Cell Lung Cancer (NSCLC) are types of Lung cancer. SCLC usually occurs in patients who have a history of heavy smoking and spreads more rapidly than NSCLC. However, about 80-85\% of all lung cancer cases are NSCLC types that mostly attack men and women. This study aimed to classify NSCLC into squamous cell carcinoma, adenocarcinoma, large cell carcinoma, and normal lung and to compare the architecture VGG19 and ResNet50 for classification NSCLC. 1000 images data for each class was used in this study from CT Scan images. Three processes were used for classification processes, such as preprocessing, classification, and validation. The resizing and grayscale process was conducted for preprocessing step to ensure all input images are uniform. The significant result was achieved in the ResNet50 architecture, with an accuracy of 98.35\% on testing data, 99.87\% on training data, and 96\% on validation data. Meanwhile, the best performance on the validation data in the normal class was that the results of precision, sensitivity, F1-score, and specificity were 100\%, 100\%, 100\%, and 100\%, respectively.",

author = "Darmawan, \{Bunga Mastiti\} and Agustin, \{Rizki Wulan\} and Farah Noviandini and Endarko",

note = "Publisher Copyright: {\textcopyright} 2022 American Institute of Physics Inc.. All rights reserved.; 4th International Conference of Science and Education Science: Integrating Rapid Technology and Whole Person Education in Science and Science Education to Encounter the New Normal Era, IConSSE 2021 ; Conference date: 07-09-2021 Through 08-09-2021",

year = "2022",

month = nov,

day = "10",

doi = "10.1063/5.0103184",

language = "English",

series = "AIP Conference Proceedings",

publisher = "American Institute of Physics Inc.",

editor = "Nugroho, \{Didit Budi\} and Andreas Setiawan and Wibowo, \{Nur Aji\} and Riyanto, \{Cucun Alep\} and Aminu, \{November Rianto\}",

booktitle = "4th International Conference of Science and Education Science, IConSSE 2021",

}

Darmawan, BM, Agustin, RW, Noviandini, F & Endarko 2022, Non-Small Cell Lung Cancer (NSCLC) Classification Using Convolutional Neural Network (CNN). in DB Nugroho, A Setiawan, NA Wibowo, CA Riyanto & NR Aminu (eds), 4th International Conference of Science and Education Science, IConSSE 2021: Integrating Rapid Technology and Whole Person Education in Science and Science Education to Encounter the New Normal Era., 050004, AIP Conference Proceedings, vol. 2542, American Institute of Physics Inc., 4th International Conference of Science and Education Science: Integrating Rapid Technology and Whole Person Education in Science and Science Education to Encounter the New Normal Era, IConSSE 2021, Salatiga, Virtual, Indonesia, 7/09/21. https://doi.org/10.1063/5.0103184

Non-Small Cell Lung Cancer (NSCLC) Classification Using Convolutional Neural Network (CNN). / Darmawan, Bunga Mastiti; Agustin, Rizki Wulan; Noviandini, Farah et al.
4th International Conference of Science and Education Science, IConSSE 2021: Integrating Rapid Technology and Whole Person Education in Science and Science Education to Encounter the New Normal Era. ed. / Didit Budi Nugroho; Andreas Setiawan; Nur Aji Wibowo; Cucun Alep Riyanto; November Rianto Aminu. American Institute of Physics Inc., 2022. 050004 (AIP Conference Proceedings; Vol. 2542).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Non-Small Cell Lung Cancer (NSCLC) Classification Using Convolutional Neural Network (CNN)

AU - Darmawan, Bunga Mastiti

AU - Agustin, Rizki Wulan

AU - Noviandini, Farah

AU - Endarko,

PY - 2022/11/10

Y1 - 2022/11/10

N2 - Small Cell Lung Cancer (SCLC) and Non-Small Cell Lung Cancer (NSCLC) are types of Lung cancer. SCLC usually occurs in patients who have a history of heavy smoking and spreads more rapidly than NSCLC. However, about 80-85% of all lung cancer cases are NSCLC types that mostly attack men and women. This study aimed to classify NSCLC into squamous cell carcinoma, adenocarcinoma, large cell carcinoma, and normal lung and to compare the architecture VGG19 and ResNet50 for classification NSCLC. 1000 images data for each class was used in this study from CT Scan images. Three processes were used for classification processes, such as preprocessing, classification, and validation. The resizing and grayscale process was conducted for preprocessing step to ensure all input images are uniform. The significant result was achieved in the ResNet50 architecture, with an accuracy of 98.35% on testing data, 99.87% on training data, and 96% on validation data. Meanwhile, the best performance on the validation data in the normal class was that the results of precision, sensitivity, F1-score, and specificity were 100%, 100%, 100%, and 100%, respectively.

AB - Small Cell Lung Cancer (SCLC) and Non-Small Cell Lung Cancer (NSCLC) are types of Lung cancer. SCLC usually occurs in patients who have a history of heavy smoking and spreads more rapidly than NSCLC. However, about 80-85% of all lung cancer cases are NSCLC types that mostly attack men and women. This study aimed to classify NSCLC into squamous cell carcinoma, adenocarcinoma, large cell carcinoma, and normal lung and to compare the architecture VGG19 and ResNet50 for classification NSCLC. 1000 images data for each class was used in this study from CT Scan images. Three processes were used for classification processes, such as preprocessing, classification, and validation. The resizing and grayscale process was conducted for preprocessing step to ensure all input images are uniform. The significant result was achieved in the ResNet50 architecture, with an accuracy of 98.35% on testing data, 99.87% on training data, and 96% on validation data. Meanwhile, the best performance on the validation data in the normal class was that the results of precision, sensitivity, F1-score, and specificity were 100%, 100%, 100%, and 100%, respectively.

UR - https://www.scopus.com/pages/publications/85142928176

U2 - 10.1063/5.0103184

DO - 10.1063/5.0103184

M3 - Conference contribution

AN - SCOPUS:85142928176

T3 - AIP Conference Proceedings

BT - 4th International Conference of Science and Education Science, IConSSE 2021

A2 - Nugroho, Didit Budi

A2 - Setiawan, Andreas

A2 - Wibowo, Nur Aji

A2 - Riyanto, Cucun Alep

A2 - Aminu, November Rianto

PB - American Institute of Physics Inc.

T2 - 4th International Conference of Science and Education Science: Integrating Rapid Technology and Whole Person Education in Science and Science Education to Encounter the New Normal Era, IConSSE 2021

Y2 - 7 September 2021 through 8 September 2021

ER -

Darmawan BM, Agustin RW, Noviandini F, Endarko. Non-Small Cell Lung Cancer (NSCLC) Classification Using Convolutional Neural Network (CNN). In Nugroho DB, Setiawan A, Wibowo NA, Riyanto CA, Aminu NR, editors, 4th International Conference of Science and Education Science, IConSSE 2021: Integrating Rapid Technology and Whole Person Education in Science and Science Education to Encounter the New Normal Era. American Institute of Physics Inc. 2022. 050004. (AIP Conference Proceedings). doi: 10.1063/5.0103184

Non-Small Cell Lung Cancer (NSCLC) Classification Using Convolutional Neural Network (CNN)

Abstract

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