The most common cancer-related cause of death globally is lung cancer. The key to effective lung cancer treatment and higher survival rates is early diagnosis. Converting a radiologist's diagnosing procedure to computer assisted results in more accurate results and an earlier diagnosis. The difficulty is that building an effective model for segmentation and classification. In this paper, we suggest a system for detecting lung cancer that makes use of a number of methods for precise and effective diagnosis. To enhance picture quality, our method pre-processes CT scan images using a Gaussian filter and contrast stretching. For the purpose of determining the borders of lung nodules with high precision, the U-Net architecture with the Adam optimizer is used. Then, a Gaussian mixture model (GMM) with EM optimisation and pixel padding is used to extract features. The rotational-based Convolutional Neural Network (RBCNN) classifier successfully categorises the nodules as benign and malignant using these form variables as inputs.

Keywords: Computer assisted system, Lung cancer detection, Lung nodule segmentation, Feature extraction, RBCONVOLUTIONAL NEURAL NETWORK, Classification.

[1] Rashmi Mothkur, B.N Veerappa (2023). Classification of lung cancer using lightweight deep neural networks. Science direct, Elsevier, 218: 1869-1877. doi: https://doi.org/10.1016/j.procs.2023.01.164.

[2] Imdad Ali, Muhammad Muzammil, Ihsan Ui Haq, Amir A. Khaliq, Suheel Abdullah (2020). Efficient Lung Nodule classification using tranferable text neural network, Journals & magazines. IEEE Access, 8: 175859- 175870, INSPEC: 19981558. doi: https://doi.org/10.1109/ACCESS.2020.3026080.

[3] Sanjukta Rani Jena, S> Thomas George, D. NArain Pnraj (2021). Lung cancer detection and classification with DGMM-RBCONVOLUTIONAL NEURAL NETWORK technique. Neural computing and applications, Springer Link, 33: 15601-15617. doi: https://link.springer.com/article/10.1007/s00521-021-06182-5.

[4] M.S. Kavitha, J. Shanthini, R. Sabitha (2019). ECM-CSD: An efficient and robust model is created for the detecting and classification of cancer stage in CT lung images using FCM and SVM techniques. Wearable computing techniques for smart health, Springer Link, 43: 73. doi: https://link.springer.com/article/10.1007/ s10916-019-1190-z.

[5] R. Pandian, V. Vedanarayanan, D.N.S. Ravi Kumarm, R. Raja Kumar (2022). Detection and classification of lung cancer using CONVOLUTIONAL NEURAL NETWORK and google Net. Measurement: Sensors, Elsevier, 24: 10588. doi: https://doi.org/10.1016/j.measen.2022.100588.

[6] Disha sharma, Gagandeep Jindal (2011). Identifying lung cancer using image processing techniques. International conference on computing Techniques and Artificial Intelligence, Citeseer, https://citeseerx.ist. psu.edu/document?repid=rep1&type=pdf&doi=4deae72f8c0287e4943457da39dd20024e99db00.

[7] K. Sankar, M. Prabhakaran (n.d). An Improved Architecture for lung cancer cell Identification using Gabor filter and Intelligence System. The International Journal of Engineering and Science, 2(4): 38-43. https://www.theijes. com/papers/v2i4/part.%20(1)/H0241038043.pdf.

[8] Muazzam Maqsood, Sadaf Yasmin, Irfan Mehmood, Maryan Bukhari, Mucheol kim (2021). An Efficient DA-Net architecture for lung cancer segmentation. AI and Big data computing, 9(13). doi: https://doi.org/10.3390/ math9131457.

[9] N. Kalaivani, N. Manimaran, S. Sophia, D. Devi  (2020). Deep Learning Based Lung Cancer  Classification. IOP Conference series: Materials Science and Engineering, IOP Science, 994: 012026. doi: https://doi.org/ 10.1088/1757-899X/994/1/012026.

[10] Thi Kieeu Ho, Jeonghwan Gwak (2020). Utilizing Knowledge Distillation in Deep learning for classification Abnormalities. IEEE Access, 8: 160749-160761. doi: https://doi.org/10.1109/ACCESS.2020.3020802.

[11] Bushara AR. (2022). A Deep Learning – Based Lung cancer classification of CT Images using Augmented Convolutional Neural Networks. ELCVIA, 21. doi: https://elcvia.cvc.uab.cat/article/view/1490.

[12] Smaranda Belciug (2020). Learning deep neural networks architecture using differential evolution. Case study: Medical imaging processing, Computers in Biology and Medicine, Elsevier, 146: 105623. doi: https://doi.org/ 10.1016/j.compbiomed.2022.105623.

[13] Mastouri Rekka, Khifa, Nawres, Neji Henda, Hautous Zannad, Saoussen (2020). Deep learning – based CAD schemes for the detection and classification of lung nodules from CT images: A survey. Journal of x-ray science and Technology, 28(4): 591-617. doi: doi: https://doi.org/10.3233/XST-200660.

[14] Yung-Shuo Kao, Jen Yang (2022). Deep learning based auto segmentation of lung tumor PET/CT scans: a systematic review. Radiomic and Artificial Intelligence, Springer Link, 10: 217-223. https://link.springer.com/ article/10.1007/s40336-022-00482-z.

[15] Pragya Chaturvedi, Anuj Jhamb, Meet Vanani, Varsha Nemade (2021). Prediction and classification of lung cancer using machine learning techniques. IOP Materials Science and Engineering. doi: https://doi.org/10.1088/17 57-899X/1099/1/012059. 

Source of Funding:

This study did not receive any grant from funding agencies in the public or not-for-profit sectors.

Competing Interests Statement:

The authors have declared no competing interests.

Consent for Publication:

The authors declare that they consented to the publication of this study.

Authors’ Contribution:

Both the authors took part in literature review, research, and manuscript writing equally.