Abstract
This work aims to design and develop an artificial neural network (ANN) architecture for the classification of cancerous tissue in the lung. A sequential model is used for the machine learning process. ReLU and Sigmoid activation functions have been used to supply weights to the model. The present work encompasses detecting and classifying the tumor cells into four categories. The four types of lung cancer nodules are adenocarcinoma, squamous-cell carcinoma, large-cell carcinoma, and small-cell carcinoma. Computed tomography (CT) and Positron emission tomography (PET) scan DICOM images are used for the classification. The proposed approach has been validated with the subset of the original dataset. A total of 6500 images have been taken in the experiment. The approach is to feed the CT scan images into ANNs and classify the image as the correct type. The dataset is provided by The Cancer Imaging Archive (TCIA). The dataset is titled “A Large-Scale CT and PET/CT Dataset for Lung Cancer Diagnosis.” The tumor cells are classified using the ANN architecture with 99.6% of validation accuracy and 4.35% loss.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Marouf A, Abu-Naser SS (2018) Predicting antibiotic susceptibility using artificial neural network. Int J Acad Pedagogical Res (IJAPR) 2(10):1–5
Li P, Wang S, Li T, Lu J, HuangFu Y, Wang D (2020) A large-scale CT and PET/CT dataset for lung cancer diagnosis. In: The cancer imaging archive
https://www.cancer.org/cancer/lung-cancer/about/what-is.html
Afana M, Ahmed J, Harb B, Abu-Nasser BS, Abu-Naser, SS (2018) Artificial neural network for forecasting car mileage per gallon in the city. Int J Adv Sci Technol 124:51–59
Prakashbabu P, Ashok Kumar D, Vithyaa T (2019) Lung cancer detection model using convolution neural network and fuzzy clustering algorithms. Int J Comput Trends Technol (IJCTT) 67(11)
Ibrahim MN, Abu-Naser SS (2019) Lung cancer detection using artificial neural network. Int J Eng Inf Syst (IJEAIS) 3(3):17–23
Kashf DWA, Okasha AN, Sahyoun NA, El-Rabi RE, Abu-Naser SS (2018) Predicting DNA lung cancer using artificial neural network. Int J Acad Pedagogical Res (IJAPR) 2(10):6–13
Ali I, Hart GR, Gunabushanam G, Liang Y, Muhammad W, Nartowt B, Kane M, Ma X, Deng J (2018) Lung nodule detection via deep reinforcement learning. Front Oncol
Senthil S, Ayshwarya B (2018) Lung cancer prediction using feed forward back propagation neural networks with optimal features. Int J Appl Eng Res 318–325
Mhaske D, Rajeswari K, Tekade R (2019) Deep learning algorithm for classification and prediction of lung cancer using CT scan images. In: 5th International conference on computing, communication, control and automation (ICCUBEA), pp 1–5
Islam MT, Aowal MA, Minhaz AT, Khalid A (2017) Abnormality detection and localization of chest X-rays using deep convolutional neural networks. arXiv
Qian Y, Guirong W (2014) Lung nodule segmentation using EM algorithm. In: Sixth international conference on intelligent human-machine systems and cybernetics, pp 20–23. https://doi.org/10.1109/IHMSC.2014.13
Li J, Zhao H, Yang Y (2020) Detection and recognition of lung nodules in medical images using chaotic ant colony algorithm. In: 12th International conference on measuring technology and mechatronics automation (ICMTMA), pp 517–522. https://doi.org/10.1109/ICMTMA50254.2020.00117
Saikia T, Kumar R, Kumar D et al. (2022) An automatic lung nodule classification system based on hybrid transfer learning approach. SN Comput. Sci 3(272). https://doi.org/10.1007/s42979-022-01167-0
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F (2013) The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26(6):1045–1057
Kumar Singh K, Kumar S, Antonakakis M, Moirogiorgou K, Deep A, Kashyap KL, Bajpai MK, Zervakis M (2022) Deep learning capabilities for the categorization of microcalcification. Int J Environ Res Public Health 19(4):2159. https://doi.org/10.3390/ijerph19042159
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Hansdah, M., Singh, K.K. (2023). Classification of Lung Nodule from CT and PET/CT Images Using Artificial Neural Network. In: Kumar Singh, K., Bajpai, M.K., Sheikh Akbari, A. (eds) Machine Vision and Augmented Intelligence. Lecture Notes in Electrical Engineering, vol 1007. Springer, Singapore. https://doi.org/10.1007/978-981-99-0189-0_50
Download citation
DOI: https://doi.org/10.1007/978-981-99-0189-0_50
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-0188-3
Online ISBN: 978-981-99-0189-0
eBook Packages: Computer ScienceComputer Science (R0)