Detection of Lung Cancer Malignancy Types on CT-Scan Using the Convolutional Neural Network Method at PHC Hospital Surabaya

Bilah Samping Artikel

PDF (English)
Diterbitkan: Jun 30, 2024
DOI: https://doi.org/10.54732/jeecs.v9i1.6

Isi Artikel Utama

Kholilul Rohman Kurniawan
Institut Sains dan Teknologi Terpadu Surabaya
Endang Setyati
Institut Sains dan Teknologi Terpadu Surabaya
Francisca Haryanti Chandra
Institut Sains dan Teknologi Terpadu Surabaya

Abstrak

There are many uses for digital image processing, ranging from tumor and cancer detection in the body to reading blood cells. The rate of lung cancer represents about 13.27% of the total cancer cases, and this shows that lung cancer is the main type of disease in men. Lung cancer is one of the most dangerous and life-threatening diseases in the world. In Indonesia, lung cancer is more often detected when patients are at an advanced stage. Therefore, in this paper, we applied Deep Learning to solve a lung cancer malignant detection system; it is used to detect and classify nodule areas. So that lung cancer detection can be obtained with accurate results. This paper explains the working system for detecting lung cancer malignancies using a Convolutional Neural Network (CNN) and the model architecture for training the dataset using the EfficientNet model. This study collected 800 lung CT images from PHC Surabaya Hospital in DICOM format. A total of 13 layers with EfficientNet architecture and classification layers for each type of cancer class have been used in the model. The experimental results of the model achieved satisfactory results with an accuracy of 99.46%, with a maximum epoch of 30 and a mini-batch size of 128.

Rincian Artikel

Cara Mengutip
Kurniawan, K. R., Setyati, E., & Chandra, F. H. (2024). Detection of Lung Cancer Malignancy Types on CT-Scan Using the Convolutional Neural Network Method at PHC Hospital Surabaya. JEECS (Journal of Electrical Engineering and Computer Sciences), 9(1), 53–60. https://doi.org/10.54732/jeecs.v9i1.6
Terbitan
Vol 9 No 1 (2024): JEECS (Journal of Electrical Engineering and Computer Sciences)
Bagian
Articles
Creative Commons License

Artikel ini berlisensi Creative Commons Attribution 4.0 International License.

Referensi

H. F. Al-Yasriy, M. S. Al-Husieny, F. Y. Mohsen, E. A. Khalil, and Z. S. Hassan, “Diagnosis of Lung Cancer Based on CT Scans Using CNN,” IOP Conference Series: Materials Science and Engineering, vol. 928, no. 2, pp. 1–9, 2020, doi: 10.1088/1757-899X/928/2/022035. DOI: https://doi.org/10.1088/1757-899X/928/2/022035

I. Iqbalawaty et al., “Profil hasil pemeriksaan CT-Scan pada pasien tumor paru di Bagian Radiologi RSUD Dr. Zainoel Abidin periode Juli 2018-Oktober 2018,” Intisari Sains Medis, vol. 10, no. 3, pp. 625–630, 2019, doi: 10.15562/ism.v10i3.661. DOI: https://doi.org/10.15562/ism.v10i3.661

C. C. Nguyen, G. S. Tran, V. T. Nguyen, J. C. Burie, and T. P. Nghiem, “Pulmonary Nodule Detection Based on Faster R-CNN with Adaptive Anchor Box,” IEEE Access, vol. 9, pp. 154740–154751, 2021, doi: 10.1109/ACCESS.2021.3128942. DOI: https://doi.org/10.1109/ACCESS.2021.3128942

A. Jawaid, J. Hafeez, S. Khan, and A. Ur Rehman, “Lung Cancer Detection using Artificial Neural Network on Android,” in 2023 Global Conference on Wireless and Optical Technologies, GCWOT 2023, 2023, pp. 17–23, doi: 10.1109/GCWOT57803.2023.10064658. DOI: https://doi.org/10.1109/GCWOT57803.2023.10064658

S. ShaikParveen and C. Kavitha, “Classification of Lung Cancer Nodules using SVM Kernels,” International Journal of Computer Applications, vol. 95, no. 25, pp. 25–28, 2014, doi: 10.5120/16751-7013. DOI: https://doi.org/10.5120/16751-7013

S. Ganesan, T. S. Subashini, and K. Jayalakshmi, “Classification of X-rays using statistical moments and SVM,” International Conference on Communication and Signal Processing, ICCSP 2014 - Proceedings, pp. 1109–1112, Nov. 2014, doi: 10.1109/ICCSP.2014.6950020. DOI: https://doi.org/10.1109/ICCSP.2014.6950020

P. Thamilselvan and J. G. R. Sathiaseelan, “Detection and Classification of Lung Cancer MRI Images by using Enhanced K Nearest Neighbor Algorithm,” Indian Journal of Science and Technology, vol. 9, no. 43, pp. 1–7, Nov. 2016, doi: 10.17485/IJST/2016/V9I43/104642. DOI: https://doi.org/10.17485/ijst/2016/v9i43/104642

J. M. Diaz, R. C. Pinon, and G. Solano, “Lung cancer classification using genetic algorithm to optimize prediction models,” IISA 2014 - 5th International Conference on Information, Intelligence, Systems and Applications, 2014, doi: 10.1109/IISA.2014.6878770. DOI: https://doi.org/10.1109/IISA.2014.6878770

A. Rifa’i and Y. Prabowo, “Diagnosis Kanker Paru-Paru dengan Sistem Fuzzy,” Krea-TIF: Jurnal Teknik Informatika , vol. 10, no. 1, pp. 19–28, 2022, doi: 10.32832/kreatif.v10i1.6317.

M. A. S. Durai and N. C. S. . Iyengar, “Effective Analysis and Diagnosis of Lung Cancer,” vol. 2, no. 6, pp. 2102–2108, 2010.

H. Sharma, J. S. Jain, P. Bansal, and S. Gupta, “Feature extraction and classification of chest X-ray images using CNN to detect pneumonia,” Proceedings of the Confluence 2020 - 10th International Conference on Cloud Computing, Data Science and Engineering, pp. 227–231, Jan. 2020, doi: 10.1109/CONFLUENCE47617.2020.9057809. DOI: https://doi.org/10.1109/Confluence47617.2020.9057809

A. Asuntha and A. Srinivasan, “Deep learning for lung Cancer detection and classification,” Multimedia Tools and Applications, vol. 79, no. 11–12, pp. 7731–7762, Mar. 2020, doi: 10.1007/S11042-019-08394-3/METRICS. DOI: https://doi.org/10.1007/s11042-019-08394-3

Q. Z. Song, L. Zhao, X. K. Luo, and X. C. Dou, “Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images,” Journal of healthcare engineering, vol. 2017, 2017, doi: 10.1155/2017/8314740. DOI: https://doi.org/10.1155/2017/8314740

A. Angel mary and K. K. Thanammal, “Lung cancer detection via deep learning-based pyramid network with honey badger algorithm,” Measurement: Sensors, vol. 31, p. 100993, Feb. 2024, doi: 10.1016/J.MEASEN.2023.100993. DOI: https://doi.org/10.1016/j.measen.2023.100993

X. Yang et al., “Fine-grained recurrent neural networks for automatic prostate segmentation in ultrasound images,” in AAAI Conference on Artificial Intelligence, 2017, pp. 1633–1639, doi: 10.1609/aaai.v31i1.10761. DOI: https://doi.org/10.1609/aaai.v31i1.10761

H. Chen et al., “Ultrasound Standard Plane Detection Using a Composite Neural Network Framework,” IEEE Transactions on Cybernetics, vol. 47, no. 6, pp. 1576–1583, Jun. 2017, doi: 10.1109/TCYB.2017.2685080. DOI: https://doi.org/10.1109/TCYB.2017.2685080

D. Nie, L. Wang, E. Adeli, C. Lao, W. Lin, and D. Shen, “3-D Fully Convolutional Networks for Multimodal Isointense Infant Brain Image Segmentation,” IEEE Transactions on Cybernetics, vol. 49, no. 3, pp. 1123–1136, Mar. 2019, doi: 10.1109/TCYB.2018.2797905. DOI: https://doi.org/10.1109/TCYB.2018.2797905

X. Li, H. Chen, X. Qi, Q. Dou, C. W. Fu, and P. A. Heng, “H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes,” IEEE Transactions on Medical Imaging, vol. 37, no. 12, pp. 2663–2674, Dec. 2018, doi: 10.1109/TMI.2018.2845918. DOI: https://doi.org/10.1109/TMI.2018.2845918

X. Wang et al., “Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis,” IEEE Transactions on Cybernetics, vol. 50, no. 9, pp. 3950–3962, Sep. 2020, doi: 10.1109/TCYB.2019.2935141. DOI: https://doi.org/10.1109/TCYB.2019.2935141