Advanced Computer Vision Model for Lung Tumor Segmentation and Detection in Radiology and Histopathology Images

Sheeja T.S; Arun Chokkalingam

doi:10.54392/irjmt25412

Authors

Sheeja T.S Department of Biomedical Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India Author
Arun Chokkalingam Department of Biomedical Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India Author

DOI:

https://doi.org/10.54392/irjmt25412

Keywords:

Lung tumor detection, Tumor segmentation, Feed Forward Neural Network (FFNN), Binary Gannet Optimizer (BGO), MATLAB simulation, Advanced Computer Vision Model

Abstract

For effective treatment scheme and initial diagnosis, it is important to segue and detect the lung tumors. Tumor variability, low contrast and overlapping tissue structure are some problems with traditional methods that make it challenging to accurately and detect it. So, a newly advanced computer vision model that uses a Binary Gennet Optimizer Tund Forward Neural Network (BGO-FFNN) method is used to sort tumors. The dataset contains anotate CT scan and histopathology slides that were taken from publicly available repository. Gabber filters are used in preprosauting to get rid of noise and strengthen the opposite, making it easier to see the edges of the tumor. Multi-scale edge-watthers' techniques work together for different tumor areas simultaneously. Local binary pattern (LBP) is used to achieve texture features, which are important to explain the difference between a variety of tumors. BGO is used to improve FFNN, and then classification performance is tested. The results showed that the suggested model improves with recall (94.48%), F1-score (97.21%), accuracy (95.14%), and accurate (99.71%). Results suggest that the proposed advanced computer vision model works better than standard people. This hybrid model creates a major difference on how well the tumor is found and classified. It is very likely to use in the clinic and helps radiologists and pathologists assess and diagnose the exact tumor. These enrichment improves clinical accuracy and streamlines the workflow in clinical settings. As technology is developed, further integration of artificial intelligence in medical imaging can lead to more significant progress in patient results.

References

A. Shimazaki, D. Ueda, A. Choppin, A. Yamamoto, T. Honjo, Y. Shimahara, Y. Miki, Deep learning-based algorithm for lung cancer detection on chest radiographs using the segmentation method. Scientific Reports, 12(1), (2022) 727. https://doi.org/10.1038/s41598-021-04667-w

S.P. Primakov, A. Ibrahim, J.E. van Timmeren, G. Wu, S.A. Keek, M. Beuque, R.W. Granzier, E. Lavrova, M. Scrivener, S. Sanduleanu, E. Kayan, Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications, 13(1), (2022) 3423. https://doi.org/10.1038/s41467-022-30841-3

O. Akter, M.A. Moni, M.M. Islam, J.M. Quinn, A.H.M. Kamal, Lung cancer detection using enhanced segmentation accuracy. Applied Intelligence, 51, (2021) 3391-3404. https://doi.org/10.1007/s10489-020-02046-y

R. Mahum, A.S. Al-Salman, Lung-RetinaNet: Lung cancer detection using a RetinaNet with multi-scale feature fusion and context module. IEEE Access, 11, (2023) 53850-53861. https://doi.org/10.1109/ACCESS.2023.3281259

I. Nazir, I.U. Haq, M.M. Khan, M.B. Qureshi, H. Ullah, S. Butt, Efficient pre-processing and segmentation for lung cancer detection using fused CT images. Electronics, 11(1), (2021) 34. https://doi.org/10.3390/electronics11010034

K.V. Rani, S.J. Jawhar, Novel technology for lung tumor detection using nanoimage. IETE Journal of Research, 67(5), (2021) 699-713. https://doi.org/10.1080/03772063.2019.1565955

P. Dutande, U. Baid, S. Talbar, Deep residual separable convolutional neural network for lung tumor segmentation. Computers in Biology and Medicine, 141, (2022) 105161. https://doi.org/10.1016/j.compbiomed.2021.105161

J. Chamberlin, M.R. Kocher, J. Waltz, M. Snoddy, N.F. Stringer, J. Stephenson, P. Sahbaee, P. Sharma, S. Rapaka, U.J. Schoepf, A.F. Abadia, J. Sperl, P. Hoelzer, M. Mercer, N. Somayaji, G. Aquino, J.R. Burt, Automated detection of lung nodules and coronary artery calcium using artificial intelligence on low-dose CT scans for lung cancer screening: accuracy and prognostic value. BMC Medicine, 19, (2021) 1-14. https://doi.org/10.1186/s12916-021-01928-3

R.R. Savjani, M. Lauria, S. Bose, J. Deng, Y. Yuan, V. Andrearczyk, Automated tumor segmentation in radiotherapy. Seminars in Radiation Oncology, 32(4), (2022) 319-329. https://doi.org/10.1016/j.semradonc.2022.06.002

S. Avinash, H.N. Naveen Kumar, M.S. Guru Prasad, R. Mohan Naik, G. Parveen, Early detection of malignant tumor in lungs using feed-forward neural network and K-nearest neighbor classifier. SN Computer Science, 4(2), (2023) 195. https://doi.org/10.1007/s42979-022-01606-y

C. Venkatesh, P. Bojja, A dynamic optimization and deep learning technique for detection of lung cancer in CT images and data access through Internet of Things. Wireless Personal Communications, 125(3), (2022) 2621–2646. https://doi.org/10.1007/s11277-022-09676-0

M.D. Javeed, R. Nagaraju, R. Chandrasekaran, G. Rajulu, P. Tumuluru, M. Ramesh, S.K. Suman, R. Shrivastava, Brain tumor segmentation and classification with hybrid clustering, probabilistic neural networks. Journal of Intelligent & Fuzzy Systems, 45(4), (2023) 6485-6500. https://doi.org/10.3233/JIFS-232493

G. Geethu Lakshmi, P. Nagaraj, (2025) Lung cancer detection and classification using optimized CNN features and Squeeze-Inception-ResNeXt model. Computational Biology and Chemistry, 117, 108437. https://doi.org/10.1016/j.compbiolchem.2025.108437

H. Liz-Lopez, A.A. de Sojo-Hernández, S. D’Antonio-Maceiras, M.A. Diaz-Martinez, D. Camacho, Deep learning innovations in the detection of lung cancer: Advances, trends, and open challenges. Cognitive Computation, 17(2), (2025) 67. https://doi.org/10.1007/s12559-025-10408-2

M. Alavinejad, M. Shirzad, M.J. Javid-Naderi, A. Rahdar, S. Fathi-Karkan, S. Pandey, Smart nanomedicines powered by artificial intelligence: a breakthrough in lung cancer diagnosis and treatment. Medical Oncology, 42(5), (2025) 134. https://doi.org/10.1007/s12032-025-02680-x

D. Xiang, B. Zhang, Y. Lu, S. Deng, Modality-specific segmentation network for lung tumor segmentation in PET-CT images. IEEE Journal of Biomedical and Health Informatics, 27(3), (2022) 1237–1248. https://doi.org/10.1109/JBHI.2022.3186275

X. Fu, L. Bi, A. Kumar, M. Fulham, J. Kim, Multimodal spatial attention module for targeting multimodal PET-CT lung tumor segmentation. IEEE Journal of Biomedical and Health Informatics, 25(9), (2021) 3507–3516. https://doi.org/10.1109/JBHI.2021.3059453

J. Yang, B. Wu, L. Li, P. Cao, O. Zaiane, MSDS-UNet: A multi-scale deeply supervised 3D U-Net for automatic segmentation of lung tumor in CT. Computerized Medical Imaging and Graphics, 92, (2021) 101957. https://doi.org/10.1016/j.compmedimag.2021.101957

P. Princy Magdaline, T.R. Ganesh Babu, R. Praveena, R. Khowshalya, A Hybrid Deep Learning Model Combining AgresNet, YOLO, and CNN for Lung Tumor Segmentation and Classification. Journal of Innovative Image Processing, 6(4), (2025) 472–498. https://doi.org/10.36548/jiip.2024.4.009

V.R. Nitha, S.S.V. Chandra, ExtRanFS: An automated lung cancer malignancy detection system using extremely randomized feature selector. Diagnostics, 13(13), (2023) 2206. https://doi.org/10.3390/diagnostics13132206

H.T. Gayap, M.A. Akhloufi, Deep machine learning for medical diagnosis, application to lung cancer detection: a review. Biomed Informatics, 4(1), (2024) 236–284. https://doi.org/10.3390/biomedinformatics4010015

M. Kashyap, X. Wang, N.Panjwani, M. Hasan, Q. Zhang, C. Huang, K. Bush, A. Chin, L.K. Vitzthum, P. Dong, S. Zaky, Automated Deep Learning-Based Detection and Segmentation of Lung Tumors at CT. Radiology, 314(1), (2025) e233029. https://doi.org/10.1148/radiol.233029

S. Vijh, P. Gaurav, H.M. Pandey, Hybrid bio-inspired algorithm and convolutional neural network for automatic lung tumor detection. Neural Computing and Applications, 35(33), (2023) 23711–23724. https://doi.org/10.1007/s00521-020-05362-z

S. Momin, Y. Lei, Z. Tian, T. Wang, J. Roper, A.H. Kesarwala, K. Higgins, J.D. Bradley, T. Liu, X. Yang, Lung tumor segmentation in 4D CT images using motion convolutional neural networks. Medical Physics, 48(11), (2021) 7141–7153. https://doi.org/10.1002/mp.15204

R. Raza, F. Zulfiqar, M.O. Khan, M. Arif, A. Alvi, M.A. Iftikhar, T. Alam, Lung-EffNet: Lung cancer classification using EfficientNet from CT-scan images. Engineering Applications of Artificial Intelligence, 126, (2023).106902. https://doi.org/10.1016/j.engappai.2023.106902

Tian, Q., Wu, Y., Ren, X. and Razmjooy, N. (2021). 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, p.102761. https://doi.org/10.1016/j.bspc.2021.102761

T. Meraj, H.T. Rauf, S. Zahoor, A. Hassan, M.I. Lali, L. Ali, S.A.C. Bukhari, U. Shoaib, Lung nodules detection using semantic segmentation and classification with optimal features. Neural Computing and Applications, 33, (2021) 10737–10750. https://doi.org/10.1007/s00521-020-04870-2

J. Soltani-Nabipour, A. Khorshidi, B. Noorian, Lung tumor segmentation using improved region growing algorithm. Nuclear Engineering and Technology, 52(10), (2020) 2313–2319. https://doi.org/10.1016/j.net.2020.03.011

P.K.F. Chiu, X. Shen, G. Wang, C.L. Ho, C.H. Leung, C.F. Ng, K.S. Choi, J.Y.C. Teoh, Enhancement of prostate cancer diagnosis by machine learning techniques: an algorithm development and validation study. Prostate Cancer and Prostatic Diseases, 25(4), (2022) 672–676. https://doi.org/10.1038/s41391-021-00429-x

P.K. Balasubramanian, W.C. Lai, G.H. Seng, J. Selvaraj, Apestnet with mask R-CNN for liver tumor segmentation and classification. Cancers, 15(2), (2023) 330. https://doi.org/10.3390/cancers15020330

S. Saha Roy, S. Roy, P. Mukherjee, A. Halder Roy, An automated liver tumour segmentation and classification model by deep learning-based approaches. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 11(3), (2023) 638-650. https://doi.org/10.1080/21681163.2022.2099300

Y. Akbari, F. Abdullakutty, S. Al Maadeed, A. Bouridane, R. Hamoudi, Breast cancer detection based on histological images using fusion of diffusion model outputs. Scientific Reports, 15(1), (2025) 21463. https://doi.org/10.1038/s41598-025-05744-0

R. Pradeep Kumar Reddy, R. Lakshmi Pravallika, A spatially constrained density-weighted clustering method for brain tumor segmentation in MRI images. International Research Journal of Multidisciplinary Technovation, 7(3), (2025) 345–364. https://doi.org/10.54392/irjmt25325

A. Raza, A. Guzzo, M. Ianni, R. Lappano, A. Zanolini, M. Maggiolini, G. Fortino, Federated learning in radiomics: A comprehensive meta-survey on medical image analysis. Computer Methods and Programs in Biomedicine, 267, (2025) 108768. https://doi.org/10.1016/j.cmpb.2025.108768

M. Shekhar, S. Khetavath, Implementing lung cancer diagnosis framework in early stages using segmentation procedures and adaptive recurrent convolution neural network with region attention for classification. Sensing and Imaging, 26(43), (2025) 1–38. https://doi.org/10.1007/s11220-025-00572-y

K.V. Aishwarya, A. Asuntha, A survey on comparative study of lung nodules applying machine learning and deep learning techniques. Multimedia Tools and Applications, 84(5), (2025) 2127–2181. https://doi.org/10.1007/s11042-024-20009-0

M. Pallumeera, J.C. Giang, R. Singh, N.S. Pracha, M.S. Makary, Evolving and novel applications of artificial intelligence in cancer imaging. Cancers, 17(9), (2025) 1510. https://doi.org/10.3390/cancers17091510

S.C. Kotoulas, D. Spyratos, K. Porpodis, K. Domvri, A. Boutou, Kaimakamis, E. C. Mouratidou, V. Dourliou, K.Tsakiri, A. Sakkou, A. Marneri, E. Angeloudi, I. Papagiouvanni, A. Michailidou, K. Malandris, K.C. Mourelatos, A. Tsantos, A. Pataka, A thorough review of the clinical applications of artificial intelligence in lung cancer. Cancers, 17(5), (2025) 882. https://doi.org/10.3390/cancers17050882

S.U. Aswathy, P.P. Fathimathul Rajeena, A. Abraham, D. Stephen, Deep learning-based BoVW–CRNN model for lung tumor detection in nano-segmented CT images. Electronics, 12(1), (2022) 14. https://doi.org/10.3390/electronics12010014

I. Naseer, S. Akram, T. Masood, M. Rashid, A. Jaffar, Lung cancer classification using modified U-Net based lobe segmentation and nodule detection. IEEE Access, 11, (2023) 60279–60291. https://doi.org/10.1109/ACCESS.2023.3285821