Lung Cancer Detection Using a Modified Convolutional Neural Network (CNN)

Cari Cari, Mohtar Yunianto, Aisyah Ajibah Rahmah

Abstract

Image processing is used to classify lung images with malignant or normal nodules. The Convolutional Neural Network (CNN) method is often used to classify images. This study uses a modified CNN architecture with various layers, filters, batch size, dropout, and epoch values. Variations were made to determine the best accuracy value and reduce the overfitting value of the proposed CNN architecture. This study implements the method using the Keras library with the Python programming language. The data is in the form of CT-Scan images of lung cancer and normal lungs. The results of several experiments from the proposed model produce an accuracy value of 95% using three layers, 128 filters on the first layer, 256 on the second layer, and 512 filters on the third layer, then with 32 batch sizes, 0.5 dropout.

Keywords

lung cancer; detection; CT-Scan; python; convolutional neural network

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References

Rachmaniar N, S., & Fuad, Y. (2017). Stabilitas Sistem Dinamik Pertumbuhan Sel Kanker dengan Terapi Radiasi. Jurnal Ilmiah Matematika Volume, 3(6). 2 Joseph, J., & Rotty, L. W. (2020). Kanker Paru: Laporan Kasus. Medical Scope Journal, 2(1). 3 Mao, Y., Yang, D., He, J., & Krasna, M. J. (2016). Epidemiology of lung cancer. Surgical Oncology Clinics, 25(3), 439-445. 4 Lakshmanaprabu, S. K., Mohanty, S. N., Shankar, K., Arunkumar, N., & Ramirez, G. (2019). Optimal deep learning model for classification of lung cancer on CT images. Future Generation Computer Systems, 92, 374-382. 5 Thabsheera, A. A., Thasleema, T. M., & Rajesh, R. (2019). Lung cancer detection using CT scan images: A review on various image processing techniques. In Data Analytics and Learning (pp. 413-419). Springer, Singapore. 6 Polat, H., & Danaei Mehr, H. (2019). Classification of pulmonary CT images by using hybrid 3D-deep convolutional neural network architecture. Applied Sciences, 9(5), 940. 7 Dong, Y., Pan, Y., Zhang, J., & Xu, W. (2017). Learning to read chest X-Ray images from 16000+ example using CNN. In 2017 IEEEA/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) (pp. 51-57). IEEE. 8 Listyalina, L., Utari, E. L., & Puspaningtyas, D. E. (2020). PENENTUAN PENYAKIT PARU DENGAN MENGGUNAKAN JARINGAN SARAF TIRUAN. Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, 11(1), 233-240. 9 Setiawan, W., Utoyo, M. I., & Rulaningtyas, R. (2019). Classification of neovascularization using convolutional neural network model. TELKOMNIKA, 17(1), 463-472. 10 Noman, N. (2020). A shallow introduction to deep neural networks. Deep Neural Evolution: Deep Learning with Evolutionary Computation, 35-63. 11 Ilahiyah, S., & Nilogiri, A. (2018). Implementasi Deep Learning Pada Identifikasi Jenis Tumbuhan Berdasarkan Citra Daun Menggunakan Convolutional Neural Network. JUSTINDO (Jurnal Sistem Dan Teknologi Informasi Indonesia), 3(2), 49-56. 12 Ker, J., Wang, L., Rao, J., & Lim, T. (2017). Deep learning applications in medical image analysis. Ieee Access, 6, 9375-9389. 13 Ding, B., Qian, H., & Zhou, J. (2018, June). Activation functions and their characteristics in deep neural networks. In 2018 Chinese control and decision conference (CCDC) (pp. 1836-1841). IEEE. 14 Peryanto, A., Yudhana, A., & Umar, R. (2020). Rancang Bangun Klasifikasi Citra Dengan Teknologi Deep Learning Berbasis Metode Convolutional Neural Network. Format J. Ilm. Tek. Inform, 8(2), 138. 15 Kalash, M., Rochan, M., Mohammed, N., Bruce, N. D., Wang, Y., & Iqbal, F. (2018, February). Malware classification with deep convolutional neural networks. In 2018 9th IFIP international conference on new technologies, mobility and security (NTMS) (pp. 1-5). IEEE. 16 Coşkun, M., Uçar, A., Yildirim, Ö., & Demir, Y. (2017, November). Face recognition based on convolutional neural network. In 2017 International Conference on Modern Electrical and Energy Systems (MEES) (pp. 376-379). IEEE. 17 Rasamoelina, A. D., Adjailia, F., & Sinčák, P. (2020, January). A review of activation function for artificial neural network. In 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI) (pp. 281-286). IEEE. 18 Foeady, A. Z., Novitasari, D. C. R., Asyhar, A. H., & Firmansjah, M. (2018). Automated Diagnosis System of Diabetic Retinopathy Using GLCM Method and SVM Classifier. 2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI). 19 Cheng, T. Y. D., Cramb, S. M., Baade, P. D., Youlden, D. R., Nwogu, C., & Reid, M. E. (2016). The international epidemiology of lung cancer: latest trends, disparities, and tumor characteristics. Journal of Thoracic Oncology, 11(10), 1653-1671. 20 ALEnezi, N. S. A. (2019). A method of skin disease detection using image processing and machine learning. Procedia Computer Science, 163, 85-92. 21 Sharma, A., Shinde, S., Shaikh, I. I., Vyas, M., & Rani, S. (2021, February). Machine learning approach for detection of diabetic retinopathy with improved pre-processing. In 2021 International conference on computing, communication, and intelligent systems (ICCCIS) (pp. 517-522). IEEE. 22 Raihan-Al-Masud, M., & Mondal, M. R. H. (2020). Data-driven diagnosis of spinal abnormalities using feature selection and machine learning algorithms. Plos one, 15(2), e0228422.

DOI: https://doi.org/10.13057/ijap.v14i1.77032

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