Authors :
Atmadeepa Srimany; Sujan Das; Debraj Paul
Volume/Issue :
Volume 9 - 2024, Issue 11 - November
Google Scholar :
https://tinyurl.com/ytuw28ms
Scribd :
https://tinyurl.com/37crtaxe
DOI :
https://doi.org/10.5281/zenodo.14524865
Abstract :
Lung cancer accounts for 1.8 million deaths
yearly from cancer-related causes worldwide, and its five-
year rate of recovery is only 19%. Though traditional
techniques like CT scans, biopsies or manual analysis of
cell pattern evaluation are susceptible to inefficiency and
mistakes made by people, early discovery greatly
improves the health of patients. Digitalimage processing
deals with applying computer algorithms to manipulate
digital images. For many applications, including image
compression, object detection, and face recognition, itis a
necessary preprocessing step. This review will look at the
most recent developments, difficulties, and potential
paths for image processing's use in lung cancer cell
identification.
Keywords :
Lung Cancer, Cancer Identification, Malignant Tumour,Digital Image Processing.
References :
- AR, B. and RS, V.K., 2022. A deep learning-based lung cancer classification of CT images using augmented convolutional neural networks. ELCVIA. Electronic letters on computer vision and image analysis, 21(1), pp.0130-142.
- Asuntha, A. and Srinivasan, A., 2020. Deep learning for lung Cancer detection and classification. Multimedia Tools and Applications, 79(11), pp.7731-7762.
- Chaunzwa, T.L., Hosny, A., Xu, Y., Shafer, A., Diao, N., Lanuti, M., Christiani, D.C., Mak, R.H. and Aerts, H.J., 2021. Deep learning classification of lung cancer histology using CT images. Scientific reports, 11(1), pp.1-12.
- Han, Y., Ma, Y., Wu, Z., Zhang, F., Zheng, D., Liu, X., Tao, L., Liang, Z., Yang, Z., Li, X. and Huang, J., 2021. Histologic subtype classification of non-small cell lung cancer using PET/CT images. European journal of nuclear medicine and molecular imaging, 48, pp.350-360.
- Hatuwal, B.K. and Thapa, H.C., 2020. Lung cancer detection using convolutional neural network on histopathological images. Int. J. Comput. Trends Technol, 68(10), pp.21-24.
- Jain, S.N. and Patil, B.G., 2014. Cancer Cells Detection Using Digital Image Processing Methods. Available from: https://www.researchgate.net/publication/281365370_Cancer_ Cells_Detection_Using_Digital_Image_Processing_ssMethods [Accessed on: 1 October 2024].
- Khan, M.A., Rubab, S., Kashif, A., Sharif, M.I., Muhammad, N., Shah, J.H., Zhang, Y.D. and Satapathy, S.C., 2020. Lungs cancer classification from CT images: An integrated design of contrast based classical features fusion and selection. Pattern Recognition Letters, 129, pp.77-85.
- Mehmood, S., Ghazal, T.M., Khan, M.A., Zubair, M., Naseem, M.T., Faiz, T. and Ahmad, M., 2022. Malignancy detection in lung and colon histopathology images using transfer learning with class selective image processing. IEEE Access, 10, pp.25657-25668.
- Nanglia, P., Kumar, S., Mahajan, A.N., Singh, P. and Rathee, D., 2021. A hybrid algorithm for lung cancer classification using SVM and Neural Networks. ICT Express, 7(3), pp.335-341.
- Parikh, P.M., Ranade, A.A., Govind, B., Ghadyalpatil, N., Singh, R., Bharath, R., Bhattacharyya, G.S., Koyande, S., Singhal, M., Vora, A., Verma, A. and Hingmire, S., 2016. Lung cancer in India: Current status and promising strategies. South Asian Journal of Cancer, 5(3), pp.93–95.
- Shakeel, P.M., Burhanuddin, M.A. and Desa, M.I., 2022. Automatic lung cancer detection from CT image using improved deep neural network and ensemble classifier. Neural Computing and Applications, pp.1-14.
- Shin, H., Oh, S., Hong, S., Kang, M., Kang, D., Ji, Y.G., Choi, B.H., Kang, K.W., Jeong, H., Park, Y. and Hong, S., 2020. Early-stage lung cancer diagnosis by deep learning- based spectroscopic analysis of circulating exosomes. ACS nano, 14(5), pp.5435-5444.
- Thakur, S.K., Singh, D.P. and Choudhary, J., 2020. Lung cancer identification: a review on detection and classification. Cancer and Metastasis Reviews, 39(3), pp.989-998.
Lung cancer accounts for 1.8 million deaths
yearly from cancer-related causes worldwide, and its five-
year rate of recovery is only 19%. Though traditional
techniques like CT scans, biopsies or manual analysis of
cell pattern evaluation are susceptible to inefficiency and
mistakes made by people, early discovery greatly
improves the health of patients. Digitalimage processing
deals with applying computer algorithms to manipulate
digital images. For many applications, including image
compression, object detection, and face recognition, itis a
necessary preprocessing step. This review will look at the
most recent developments, difficulties, and potential
paths for image processing's use in lung cancer cell
identification.
Keywords :
Lung Cancer, Cancer Identification, Malignant Tumour,Digital Image Processing.