Enhancing and Detecting the Lung Cancer using Deep Learning

Main Article Content

Madhavi Aluka
Rajeev Dixit
Pankaj Kumar


In the medical sector cancer detection became the most challenging task. Here a lot of research is carried out by the scientific fraternity. Most medical issues are getting better answers because to modern technology like artificial intelligence and models based on neural networks. In this the first half part of the paper discuss about the CNN model by using regularization and augmentation techniques for getting the better accuracy result. The second part delas with developing and demonstrating an application for detecting the lung cancer using the deep learning (DL). Here the application is built using flask which works based on the Python programming language. This acts as an application programming interface (API) between the cloud server and the proposed application mod el. Heroku cloud platform was used as a service base to launch the software and to use the application with highest reliability. The internal functionality of the anticipated model is created on convolutional neural network (CNN) architecture with ten layers to obtain high accuracy. The model demonstrated a considerable training and validation accuracy of 94% and 92% respectively.

Article Details

How to Cite
Aluka, M. ., R. . Dixit, and P. . Kumar. “Enhancing and Detecting the Lung Cancer Using Deep Learning”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 3s, Mar. 2023, pp. 127-34, https://ijritcc.org/index.php/ijritcc/article/view/6173.


S. Albawi, T.A. Mohammed, and S. Al-Zawi. "Understanding of a convolutional neural network." In 2017 International Conference on Engineering and Technology (ICET), pp. 1-6. Ieee, 2017.

H. Jiang, H. Ma, W. Qian, M. Gao, and Yan Li. "An automatic detection system of lung nodules based on a multigroup patch-based deep learning network." IEEE journal of biomedical and health informatics 22, no. 4 (2017): 1227-1237.

R. Tekade, and K. Rajeswari. "Lung cancer detection and classification using deep learning." In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), pp. 1-5. IEEE, 2018.

S. Bhatia, Y. Sinha, and L. Goel. "Lung cancer detection: a deep learning approach." In Soft Computing for Problem Solving, pp. 699-705. Springer, Singapore, 2019.

R. Zhang. "Making convolutional networks shift-invariant again." In the International Conference on Machine Learning, pp. 7324-7334. PMLR, 2019.

L. Wang, W. Huang, Z. Yang, and C. Zhang. "Temporal-spatial-frequency depth extraction of brain-computer interface based on mental tasks." Biomedical Signal Processing and Control 58 (2020): 101845.

Madhavi. A, Dr.SumathiGanesan and Dr Vijaya pal Reddy “ Applying Reluand Tanh Functions for the Detection of Lung Cancer using Deep Learning”.

J. Kuruvilla, and K. Gunavathi. "Lung cancer classification using neural networks for CT images." Computer methods and programs in biomedicine 113, no. 1 (2014): 202-209.

W. Rahane, H. Dalvi, Y. Magar, A. Kalane, and S. 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.

Q. Z. Song, L. Zhao, X. L. Luo, and X. C. Dou. "Using deep learning for classification of lung nodules on computed tomography images." Journal of healthcare engineering 2017 (2017).

S. Raut, S. Patil, and G. Shelke. "Lung Cancer Detection using Machine Learning Approach." International Journal 6, no. 1 (2021).

H. Jiang, H. Ma, W. Qian, M. Gao, and Y. 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.

S.L. Fernandes, V.P. Gurupur, H. Lin, and R.J. Martis. "A novel fusion approach for early lung cancer detection using computer aided diagnosis techniques." Journal of Medical Imaging and Health Informatics 7, no. 8 (2017): 1841-1850.

C. Jacobs, E. M. Van Rikxoort, T. Twellmann, E. T. Scholten, P. A. De Jong, J. M. Kuhnigk, M. Oudkerk, H. J. De Koning, C. Schaefer-Prokop, C. and B van Ginneken. "Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images." Medical image analysis 18, no. 2 (2014): 374-384.

E. M. van Rikxoort, B. de Hoop, M. A. Viergever, M. Prokop, and B. van Ginneken, "Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection", Medical Physics, vol. 4236 no. 10, pp. 2934-2947, 2009.

N. G and G. C. D, "Unsupervised Machine Learning Based Group Head Selection and Data Collection Technique," 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), 2022, pp. 1183-1190, doi: 10.1109/ICCMC53470.2022.9753995