The Effect of Hyper Parameters on the Classification of Lung Cancer Images Using Deep Learning Methods

Year 2022, Volume: 15 Issue: 1, 258 - 268, 27.03.2022

Derya Narin , Tuğba Özge Onur

https://doi.org/10.18185/erzifbed.1006560

Cited By: 2

Abstract

Cancer is a fatal disease arised from the formation of abnormal cells as a result of random growth in the human body. Lung cancer is the frequently encountered cancer type and causes abnormal growth of lung cells. Diagnosis at an early stage substantially enhances the chance of survivability of the patient, as well as prolongs the survival time. There may even be a complete recovery. For this reason, it is of vital importance to support the diagnosis and detection of doctors and enables them to diagnose more easily and quickly. In this paper, it is aimed to detect lung cancer disease with the help of Alexnet and Resnet50 architectures, which are deep learning architectures, by using computed tomography images. In addition, the performances of the hyper-parameters of maximum epoch and batch size, which are of great importance in training the models, have been compared. According to the results obtained, the highest overall accuracy in automatic detection of lung cancer has been achieved with the AlexNet architecture. The highest overall accuracy value obtained as a result of the simulations is found to be 98.58% with the highest cycle value and the batch size are 200 and 64, respectively.

Keywords

Lung cancer, Alexnet, Resnet50, epoch, mini batch size

Supporting Institution

Scientific Research Project Fund of Bülent Ecevit University

Project Number

2021-75737790-01

Thanks

The authors are immensely grateful for the financial support of the Scientific Research Project Fund of Bülent Ecevit University numbered 2021-75737790-01.

References

• Agner, S.C., Soman, S., Libfeld, E., McDonald, M., Thomas, K., Englander, S., Madabhushi, A. 2011. "Textural Kinetics: A Novel Dynamic Contrast-enhanced (DCE)-MRI Feature for Breast Lesion Classification", Journal of Digital Imaging, 24(3), 446-463.
• Al-Antari, M. A., Han, S. M., Kim, T. S. 2020. "Evaluation of Deep Learning Detection and Classification Towards Computer-aided Diagnosis of Breast Lesions in Digital X-ray Mammograms", Computer Methods and Programs in Biomedicine, 196, 105584.
• Alilou, M., Kovalev, V., Snezhko, E., Taimouri, V. 2014. "A Comprehensive Framework for Automatic Detection of Pulmonary Nodules in Lung CT Images", Image Analysis & Stereology, 33(1), 13-27.
• Alyasriy, H.F. "The IQ-OTHNCCD Lung Cancer Dataset", https://data.mendeley.com/datasets/bhmdr45bh2/1, Last accessed: 10.08.2021
• Al-Yasriy, H. F., Al-Husieny, M. S., Mohsen, F. Y., Khalil, E. A., Hassan, Z. S. 2020. "Diagnosis of Lung Cancer Based on CT Scans Using CNN", IOP Conference Series: Materials Science and Engineering (ISCAU), Thi-Qar, Iraq, 928(2), 022035.
• Ari, A., Hanbay, D. 2018. "Deep Learning Based Brain Tumor Classification and Detection System", Turkish Journal of Electrical Engineering & Computer Sciences, 26(5), 2275-2286.
• Çevik, K., Dandıl, E. 2019. "Classification of Lung Nodules Using Convolutional Neural Networks on CT Images", 2nd International Turkish World Engineering and Science Congress, Antalya, 27-35.
• Çinar, A., Yildirim, M. 2020. "Detection of Tumors on Brain MRI Images Using the Hybrid Convolutional Neural Network Architecture", Medical Hypotheses, 139, 109684.
• Demirkazık, B. F. 2014. "Akciğer Kanserinde Bilgisayarlı Tomografi ile Tarama: Güncel Bilgiler", Türk Radyoloji Seminerleri, Hacettepe Üniversitesi Tıp Fakültesi, Radyoloji Anabilim Dalı, 290-303.
• Fanti, S., Goffin, K., Hadaschik, B. A., Herrmann, K., Maurer, T., MacLennan, S., Daniela, E. O-L. 2021. "Consensus Statements on PSMA PET/CT Response Assessment Criteria in Prostate Cancer", European Journal of Nuclear Medicine and Molecular Imaging (EJNMMI), 48(2), 469-476.
• Gray, S., Radford, A., Kingma, D. P. 2017. "Gpu Kernels For Block-sparse Weights", Technical Report.
• Hamdalla, F. K., Al-Huseiny, M. S., Mohsen, F. Y., Khalil, E. A., Hassan Z. S. 2021. "Evaluation of SVM Performance In the Detection of Lung Cancer in Marked CT Scan Dataset", Indonesian Journal of Electrical Engineering and Computer Science (IAES), 21(3), 1731-1738.
• Huang, C. Y., Ju, D. T., Chang, C. F., Reddy, P. M., Velmurugan, B. K. 2017. "A Review on the Effects of Current Chemotherapy Drugs and Natural Agents in Treating Non–Small Cell Lung Cancer", Biomedicine, 7(4), 12-23.
• Hu, J., Shen, L., Sun G. 2018. "Squeeze-and-excitation Networks", IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 7132-7141.
• Jameson, J. L., Fauci, A. S., Kasper, D. L., Hauser, S. L., Longo, D. L., Loscalzo, J. (2004). "Harrison's Principles of Internal Medicine", Mc Graw Hill, 506–516.
• Kareem, H. F. "The IQ-OTHNCCD Lung Cancer Dataset", https://www.kaggle.com/hamdallak/the-iqothnccd-lung-cancer dataset/metadata, Last accessed: 10.08.2021
• Khanmohammadi, A., Aghaie, A., Vahedi, E., Qazvini, A., Ghanei, M., Afkhami, A., Bagheri, H. 2020. "Electrochemical Biosensors for the Detection of Lung Cancer biomarkers: a review", Talanta, 206 (1), 120251.
• Krizhevsky, A., Sutskever, I., and Hinton, G. E. 2012. "Imagenet Classification with Deep Convolutional Neural Networks", Advances in Neural İnformation Processing Systems, 25, 1097-1105.
• Lindsay, G. W. 2021. "Convolutional Neural Networks As a Model of the Visual System: Past, Present, and Future", Journal of Cognitive Neuroscience, 33(10), 2017–2031.
• Narin, A., Kefeli, S. K. 2020. "Meme Kanseri Tespitinde Evrişimsel Sinir Ağı Modellerinin Performansları", Karaelmas Science and Engineering Journal, 10(2), 186-194.
• Narin, A., Özer, M., İşler, Y. 2017. "Effect of Linear and Non-linear Measurements of Heart Rate Variability in Prediction of PAF Attack", 25th Signal Processing and Communications Applications Conference (SIU), Antalya, 1-4.
• Prarthana, K. R., Bhavani, K. 2021. "Lung Cancer Classification Techniques", International Journal of Engineering Science and Computing (IJESC), 11(6), 28054-28058.
• Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Fei-Fei, L. 2015. "Imagenet Large Scale Visual Recognition Challenge", International Journal of Computer Vision, 115(3), 211-252.
• Stapelfeld, C., Dammann, C., Maser, E. 2020. "Sex‐specificity in Lung Cancer Risk", International Journal of Cancer, 146 (9), 2376-2382.
• Sun, T., Jingjing, W., Xia, L., Pingxin, L., Fen, L., Yanxia, L, Qi, G., Huiping, Z., Xiuhua, G. 2013. "Comparative Evaluation of Support Vector Machines for Computer Aided Diagnosis of Lung Cancer in CT Based on a Multi-dimensional Data Set", Computer Methods and Programs in Miomedicine, 111(2), 519-524.
• Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., Bray, F. 2021. "Global cancer statistics 2020 (GLOBOCAN)", A Cancer Journal for Clinicians, 71(3), 209-249.
• Tan, M., Deklerck, R., Jansen, B., Bister, M., Cornelis, J. 2011. "A Novel Computer‐aided Lung Nodule Detection System for CT Images", Medical Physics, 38(10), 5630-5645.
• Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T., Vapnik, V. 2000. "Feature Selection for SVMs", Advances in Neural Information Processing Systems (NIPS), 12, 668-674.
• Xie, W., She, Y., Guo, Q. 2021. "Research on Multiple Classification Based on Improved SVM Algorithm for Balanced Binary Decision Tree", Scientific Programming, 2021, 1-11.
• Xiuhua G., Tao, S., Zhigang, L. (2011). "Prediction Models for Malignant Pulmonary Nodules Based-on Texture Features of CT Image", InTech, 63-76.
• Yamashita, R., Nishio, M., Do, R. K. G., Togashi, K. 2018. "Convolutional Neural Networks: An Overview and Application In Radiology", Insights into İmaging, 9(4), 611-629.
• Zhao, B., Gamsu, G., Ginsberg, M. S., Jiang, L., Schwartz, L. H. 2003. "Automatic Detection of Small Lung Nodules on CT Utilizing a Local Density Maximum Algorithm", Journal of Applied Clinical Medical Physics, 4(3), 248-260.