Lung Cancer Detection using CT Scan Images in CNN Algorithm
  IJCTT-book-cover
 		          
 
© 2023 by IJCTT Journal
Volume-71 Issue-4
Year of Publication : 2023
Authors : Velagapudi Sreenivas, Anupoojitha Gadila, Triveni Attunuru, Snowja Chodavarapu, Sathvika kesamaneni
DOI :  10.14445/22312803/IJCTT-V71I4P111

How to Cite?

Velagapudi Sreenivas, Anupoojitha Gadila, Triveni Attunuru, Snowja Chodavarapu, Sathvika kesamaneni, "Lung Cancer Detection using CT Scan Images in CNN Algorithm," International Journal of Computer Trends and Technology, vol. 71, no. 4, pp. 91-96, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I4P111

Abstract
A terrible illnessthat claimslives quickly all around the world islung cancer.Because deaths from lung cancer are happening more frequently, the second most frequentform of cancerislung cancer, according to studies. An automated method is required to predictthe illness and save a person's life. The accuracy and quality of the images are the main factorsin this study. The enhancement step is when low-level—employing pre-processing methods that use the Gabor filter inside the Gaussian rulesto assess and enhance the image quality. Thefeatures of normal and abnormal photos are extracted using the segmentation and enhancement technique. A comparison of normalcy is performed based on shared traits. In this study, the key criteria to check for in order to accurately compare photographs are pixelspercentage and mask labelling. Because We chose to use CNN algorithmsto analyse CT scanpictures in order because the lung tissue is where this specific type of cancer frequently first appears to identify lung cancer.

Keywords
AI, Machine Learning, Optical Character Recognition (OCR), Deep Learning, Neural Networks.

Reference

[1] Taruna Aggarwal, Asna Furqan, and Kunal Kalra, “Feature Extraction and LDA Based Classification of Lung Nodules in Chest CT Scan Images,” 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Xin-Yu Jin, Yu-Chen Zhang, and Qi-Liang Jin, “Pulmonary Nodule Identification Based on CT Images Using Convolution Neural Network,” 2016 Ninth International Conference on Computational Design and Intelligence (ISCID), 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Srinivas AruKonda, and S. Soundararajan, “Investigation of Lung Cancer Detection Using 3D Convolutional Deep Neural Network,” 2020 2nd International Conference on Advances in Computing, Communication Control and Networking, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Sruthi Ignatius, and Robin Joseph, “Computer Aided Lung Cancer Detection System,” 2015 Global Conference on Communication Technologies (GCCT), 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Y.M. Huo et al., “CNN vs.DNN Comparison of the Effect of False Positive Reduction in Deep Learning Based Detection for Juxta Pleural Lung Nodules,” Conference Proceedings on Modeling and Simulationin Medicine (MSM), 2017.
[6] J.D'Cruz et al., “Detection of Lung Cancer using Genetic Algorithm and Back Propagation Neural Networks,” Computer Technology Application, vol. 6, no. 5, pp. 823-827, 2016.
[7] M. Bharathi, B.R. Sowmya, and S. Shashikala, “Lung Cancer Detection and Classification Using Deep CNN,” International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 8, no. 2, 2018.
[Google Scholar]
[8] Gao Huang et al., “Densely Connected Convolutional Networks,” 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017.
[CrossRef] [Publisher Link]
[9] Worku J. Sori et al., “DFD-Net: Lung Cancer Detection from Denoised CT Scan Picture Using Deep Learning,” Frontiers of Computer Science, vol. 15, p. 152701, 2021.
[CrossRef] [Publisher Link]
[10] Christian Jacob, Jorg Denzinger, and Rotem Golan, “Lung Nodule Identification Using Deep Convolutional Neural Network,” International Joint Conference on Neural Networks (IJCNN), 2016.
[CrossRef] [Publisher Link]
[11] Sunyi Zheng et al., “Automatic Pulmonary Nodule Detection in CT Scans using Convolutional Neural Networks Based on Maximum Intensity Projection,” IEEE Transactions on Medical Imaging, vol. 39, no. 3, pp. 797-805, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Elmar Rendon-Gonzalez, and Volodymyr Ponomaryov, “Automatic Lung Nodule Segmentation and Classification in CT Images Based on SVM,” 9 th International Kharkiv Symposium on Physics and Engineering of Microwaves, Millimeter and Submillimeter Waves, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[13] P.B. Sangamithraa, and S. Govindaraju, “Lung Tumor Detection and Classification using EK-Mean Clustering,” International Conference on Wireless Communications, Signal Processing, and Networking (WISPNET), 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Marjolein A. Heuvelmans et al., “Lung Cancer Prediction by Deep Learning to Identify Benign Lung Nodules,” Lung Cancer, vol. 154, pp. 1-4, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] PrajwalRao, Nishal Ancelotti Pereira, and Raghuram Srinivasa, “Convolutional Neural Networks for Lung Cancer Screening in Computed Tomography (CT)Scans,” 2 nd International Conference on Contemporary Computing and Informatics, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Arvind T, K. Radhika, “XGBoost Machine Learning Model-Based DDoS Attack Detection and Mitigation in an SDN Environment,” International Journal of Engineering Trends and Technology, vol. 71, no. 2, pp. 349-361, 2023.
[CrossRef] [Publisher Link]
[17] Anita Chaudhary, and Sonit Sukhraj Singh, “Multi Resolution Analysis Technique for Lung Cancer Detection in Computed Tomographic Images,” International Journal of Researching Engineering and Applied Sciences, IJREAS, vol. 2, no. 2, 2012.
[18] A Manikandarajan, and S. Sasikala, “Detection and Segmentation of Lymph Nodes for Lung Cancer Diagnosis,” National Conference on System Design and Information Processing, 2013.
[Google Scholar]
[19] Albert Chon, Peter Lu, and Niranjan Balachandar, “Deep Convolutional Neural Networks for Lung Cancer Detection,” Wavelet Recurrent Neural Networkfor Lung Cancer Classification”: 3rd ICST Computer, 2017.
[20] S. Farjana Farvin, and S. Krishna Mohan, “A Comparative Study on Lung Cancer Detection using Deep Learning Algorithms,” SSRG International Journal of Computer Science and Engineering, vol. 9, no. 5, pp. 1-4, 2022.
[CrossRef] [Publisher Link]
[21] Matko Šarić et al., “CNN-based Method for Lung Cancer Detection in Whole Slide Histopathology Images,” 2019 4th International Conference on Smart and Sustainable Technologies (SpliTech), 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Wasudeo Rahane et al., “Lung Cancer Detection Using Image Processing and Machine Learning Health Care,” 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT), 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Worawate Ausawalaithong et al., “Automatic Lung Cancer Prediction from Chest X-ray Images Using the Deep Learning Approach,” 2018 11th Biomedical Engineering International Conference (BMEiCON), 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Febr Mokhled S. Al-Tarawneh, “Lung Cancer Detection Using Image Processing Techniques,” Leonardo Electronic Journal of Practices and Technologies, 2012.
[25] Siddharth Bhatia, Yash Sinha, and Lavika Goel, “Lung Cancer Detection: A Deep Learning Approach,” Advances in Intelligent Systems and Computing, vol. 817, pp. 699–705, 2019.
[CrossRef] [Google Scholar] [Publisher Link]