Automatically Segmenting and Classifying the Lung Nodules from CT Images
Keywords:
Deep Learning, CNN, LIDC-IDRI, VGG16Abstract
Lung cancer, a widespread and potentially disastrous type of cancer, requires urgent action to prevent catastrophic consequences caused by delayed medical care. Currently, Computed Tomography (CT) scans are used to assist doctors in detecting lung nodules at an early stage. However, the accuracy of lung nodules diagnosis heavily relies on the expertise of physicians, which can lead to potential oversight of specific patients and subsequent difficulties. As a result, deep learning has emerged as a highly considered and effective method in various medical imaging fields, including lung cancer and nodules detection. In this paper, we proposed a Custom-VGG16 model to analyze CT images of lungs and accurately classify malignant lung nodules. The Custom-VGG16 model was tested using the LIDC-IDRI database to evaluate its effectiveness. The experimental results highlight the remarkable performance of the Custom-VGG16 network, achieving an accuracy rate of 95%. Furthermore, the results indicated that the Custom-VGG16 network outperforms both the VGG16 and CNN models in detecting lung nodules.
Downloads
References
Song, QingZeng, Lei Zhao, XingKe Luo, and XueChen Dou. "Using deep learning for classification of lung nodules on computed tomography images." Journal of healthcare engineering 2017 (2017).
Asuntha, A., and Andy Srinivasan. "Deep learning for lung Cancer detection and classification." Multimedia Tools and Applications 79 (2020): 7731-7762.
Sori, Worku J., Jiang Feng, Arero W. Godana, Shaohui Liu, and Demissie J. Gelmecha. "DFD-Net: lung cancer detection from denoised CT scan image using deep learning." Frontiers of Computer Science 15 (2021): 1-13.
Sreekumar, Amrit, Karthika Rajan Nair, Sneha Sudheer, H. Ganesh Nayar, and Jyothisha J. Nair. "Malignant lung nodule detection using deep learning." In 2020 International conference on communication and signal processing (ICCSP), pp. 0209-0212. IEEE, 2020.
Cengil, Emine, and Ahmet Cinar. "A deep learning based approach to lung cancer identification." In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), pp. 1-5. Ieee, 2018.
Bhatia, Siddharth, Yash Sinha, and Lavika Goel. "Lung cancer detection: a deep learning approach." In Soft Computing for Problem Solving: SocProS 2017, Volume 2, pp. 699-705. Springer Singapore, 2019.
Heuvelmans, Marjolein A., Peter MA van Ooijen, Sarim Ather, Carlos Francisco Silva, Daiwei Han, Claus Peter Heussel, William Hickes et al. "Lung cancer prediction by Deep Learning to identify benign lung nodules." Lung Cancer 154 (2021): 1-4.
Li, Yanfeng, Linlin Zhang, Houjin Chen, and Na Yang. "Lung nodule detection with deep learning in 3D thoracic MR images." IEEE Access 7 (2019): 37822-37832.
Wu, Qing, and Wenbing Zhao. "Small-cell lung cancer detection using a supervised machine learning algorithm." In 2017 international symposium on computer science and intelligent controls (ISCSIC), pp. 88-91. IEEE, 2017.
Wang, Weilun, and Goutam Chakraborty. "Deep learning for automatic identification of nodule morphology features and prediction of lung cancer." In 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST), pp. 1-6. IEEE, 2019.
Dev, Chethan, Kripa Kumar, Arjun Palathil, T. Anjali, and Vinitha Panicker. "Machine learning based approach for detection of lung cancer in dicom ct image." In Ambient Communications and Computer Systems: RACCCS-2018, pp. 161-173. Springer Singapore, 2019.
Bhandary, Abhir, G. Ananth Prabhu, Venkatesan Rajinikanth, K. Palani Thanaraj, Suresh Chandra Satapathy, David E. Robbins, Charles Shasky, Yu-Dong Zhang, João Manuel RS Tavares, and N. Sri Madhava Raja. "Deep-learning framework to detect lung abnormality–A study with chest X-Ray and lung CT scan images." Pattern Recognition Letters 129 (2020): 271-278.
Wang, Wei, Fang Liu, Xiaohui Zhi, Tong Zhang, and Chuanchao Huang. "An integrated deep learning algorithm for detecting lung nodules with low-dose ct and its application in 6g-enabled internet of medical things." IEEE Internet of Things Journal 8, no. 7 (2020): 5274-5284.
Naik, Amrita, and Damodar Reddy Edla. "Lung nodule classification on computed tomography images using deep learning." Wireless personal communications 116 (2021): 655-690.
Tian, Qingji, Yongtang Wu, Xiaojun Ren, and Navid Razmjooy. "A new optimized sequential method for lung tumor diagnosis based on deep learning and converged search and rescue algorithm." Biomedical Signal Processing and Control 68 (2021): 102761.
Das, Susmita, and Swanirbhar Majumder. "Lung cancer detection using deep learning network: A comparative analysis." In 2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), pp. 30-35. IEEE, 2020.
Rahane, Wasudeo, Himali Dalvi, Yamini Magar, Anjali Kalane, and Satyajeet Jondhale. "Lung cancer detection using image processing and machine learning healthcare." In 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT), pp. 1-5. IEEE, 2018.
Kumar, Vinod, and Brijesh Bakariya. "Classification of malignant lung cancer using deep learning." Journal of Medical Engineering & Technology 45, no. 2 (2021): 85-93.
Wang, Weilun, and Goutam Charkborty. "Automatic prognosis of lung cancer using heterogeneous deep learning models for nodule detection and eliciting its morphological features." Applied Intelligence 51 (2021): 2471-2484.
de Pinho Pinheiro, Cesar Affonso, Nadia Nedjah, and Luiza de Macedo Mourelle. "Detection and classification of pulmonary nodules using deep learning and swarm intelligence." Multimedia Tools and Applications 79 (2020): 15437-15465.
Lakshmanaprabu, S. K., Sachi Nandan Mohanty, K. Shankar, N. Arunkumar, and Gustavo Ramirez. "Optimal deep learning model for classification of lung cancer on CT images." Future Generation Computer Systems 92 (2019): 374-382.
Huang, Peng, Cheng T. Lin, Yuliang Li, Martin C. Tammemagi, Malcolm V. Brock, Sukhinder Atkar-Khattra, Yanxun Xu et al. "Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method." The Lancet Digital Health 1, no. 7 (2019): e353-e362.
Günaydin, Özge, Melike Günay, and Öznur Şengel. "Comparison of lung cancer detection algorithms." In 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), pp. 1-4. IEEE, 2019.
Avanzo, Michele, Joseph Stancanello, Giovanni Pirrone, and Giovanna Sartor. "Radiomics and deep learning in lung cancer." Strahlentherapie und Onkologie 196 (2020): 879-887.
Zhang, Qinghai, and Xiaojing Kong. "Design of automatic lung nodule detection system based on multi-scene deep learning framework." IEEE Access 8 (2020): 90380-90389.
Ausawalaithong, Worawate, Arjaree Thirach, Sanparith Marukatat, and Theerawit Wilaiprasitporn. "Automatic lung cancer prediction from chest X-ray images using the deep learning approach." In 2018 11th Biomedical Engineering International Conference (BMEiCON), pp. 1-5. IEEE, 2018.
Sujitha, R., and V. Seenivasagam. "Classification of lung cancer stages with machine learning over big data healthcare framework." Journal of Ambient Intelligence and Humanized Computing 12 (2021): 5639-5649.
Yu, Hui, Jinqiu Li, Lixin Zhang, Yuzhen Cao, Xuyao Yu, and Jinglai Sun. "Design of lung nodules segmentation and recognition algorithm based on deep learning." BMC bioinformatics 22 (2021): 1-21.
Xie, Yutong, Yong Xia, Jianpeng Zhang, Yang Song, Dagan Feng, Michael Fulham, and Weidong Cai. "Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest CT." IEEE transactions on medical imaging 38, no. 4 (2018): 991-1004.
Majidpourkhoei, Reza, Mehdi Alilou, Kambiz Majidzadeh, and Amin Babazadehsangar. "A novel deep learning framework for lung nodule detection in 3d ct images." Multimedia Tools and Applications 80, no. 20 (2021): 30539-30555.
Jenipher, V. Nisha, and S. Radhika. "A study on early prediction of lung cancer using machine learning techniques." In 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), pp. 911-916. IEEE, 2020.
Mohmmad, Sallauddin, and Suresh Kumar Sanampudi. "Tree Cutting Sound Detection Using Deep Learning Techniques Based on Mel Spectrogram and MFCC Features." In Proceedings of Third International Conference on Advances in Computer Engineering and Communication Systems: ICACECS 2022, pp. 497-512. Singapore: Springer Nature Singapore, 2023.
Zhang, Baihua, Shouliang Qi, Patrice Monkam, Chen Li, Fan Yang, Yu-Dong Yao, and Wei Qian. "Ensemble learners of multiple deep CNNs for pulmonary nodules classification using CT images." IEEE Access 7 (2019): 110358-110371.
Jiang, Hongyang, He Ma, Wei Qian, Mengdi Gao, and Yan Li. "An automatic detection system of lung nodule based on multigroup patch-based deep learning network." IEEE journal of biomedical and health informatics 22, no. 4 (2017): 1227-1237.
Mhaske, Diksha, Kannan Rajeswari, and Ruchita Tekade. "Deep learning algorithm for classification and prediction of lung cancer using ct scan images." In 2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA), pp. 1-5. IEEE, 2019.
https://www.kaggle.com/datasets/zhangweiled/lidcidri
Kollem, S., Reddy, K. R., Prasad, C. R., Chakraborty, A., Ajayan, J., Sreejith, S., &Janapati, R. (2023). AlexNet‐NDTL: Classification of MRI brain tumor images using modified AlexNet with deep transfer learning and Lipschitz‐based data augmentation. International Journal of Imaging Systems and Technology, 1-17.
Kollem, S., Ramalinga Reddy, K., Srinivasa Rao, D., Rajendra Prasad, C., Malathy, V., Ajayan, J., &Muchahary, D. (2022). Image denoising for magnetic resonance imaging medical images using improved generalized cross‐validation based on the diffusivity function. International Journal of Imaging Systems and Technology, 32(4), 1263-1285.
Sheeba, T. B. ., Hemanth, S. V. ., Devaraj, V. ., Arularasan, A. N. ., & Gopianand, M. . (2023). Digital Hash Data Encryption for IoT Financial Transactions using Blockchain Security in the Cloud. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4s), 129–134. https://doi.org/10.17762/ijritcc.v11i4s.6316
Sivakumar, D. S. (2021). Clustering and Optimization Based on Hybrid Artificial Bee Colony and Differential Evolution Algorithm in Big Data. Research Journal of Computer Systems and Engineering, 2(1), 23:27. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/15
Kshirsagar, P. R., Reddy, D. H., Dhingra, M., Dhabliya, D., & Gupta, A. (2022). A review on comparative study of 4G, 5G and 6G networks. Paper presented at the Proceedings of 5th International Conference on Contemporary Computing and Informatics, IC3I 2022, 1830-1833. doi:10.1109/IC3I56241.2022.10073385 Retrieved from www.scopus.com
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
