An Integrated CT Image Approach for Oral Cancer Analysis using DOAKC Segmentation and ORB Feature Extraction
Keywords:
Oral Cancer, Resize, Dynamic Optimal Adaptive K-Means Clustering, Oriented FAST, Rotated BRIEF (ORB), Data AugmentationAbstract
Oral cancer remains a main health concern, often diagnosed at sophisticated periods, leading to poor patient outcomes. Early
detection using medical imaging significantly improves survival rates. Manual understanding of CT images is prolonged and
prone to error, contributing to delayed diagnosis. Current diagnostic tools lack automation and accuracy in early-stage cancer
detection. This research proposes a Probabilistic Ant Optimized Decision Tree Classifier (PAODTC) model that processes
CT images for early-stage oral cancer detection. The dataset for this research is sourced from the Cancer Imaging Archive
(TCIA), an open-access cancer image repository. CT images were resized for uniformity in pre-processing. Dynamic Optimal
Adaptive K-Means Clustering is employed to effectively segment the cancerous regions within the images, allowing for
precise delineation of affected tissues and enhancing the accuracy of extracting features. Next, the Oriented FAST and
Rotated BRIEF (ORB) algorithm retrieved essential characteristics from the segmented images, enabling a robust
representation of the vital characteristics necessary for effective analysis. Data augmentation techniques, including adding
Gaussian noise, were applied to enhance data diversity. Classification utilizes the proposed PAODTC model, which
integrates AdaBoost, Decision Tree, and a modified Ant Colony Optimization for classification. This Python model, which
was created and put into use, successfully distinguishes between malignant and non-cancerous tissues using the features that
were retrieved. The results established that the K-means clustering model significantly improved the performance in terms
of segmentation dice score (0.9288), average precision (0.9366), and evaluation time (0.0938). PAODTC significantly
outperforms existing approaches in accuracy and detection efficiency.
Downloads
References
1. Kazemi, K.S., Kazemi, P., Mivehchi, H., Nasiri, K., EshaghHoseini, S.S., Nejati, S.T., Pour Bahrami, P., Golestani, S. and NabiAfjadi, M., 2024. Photodynamic therapy: a novel approach for head and neck cancer treatment focusing on the oral cavity. Biological Procedures Online, 26(1), p.25. https://doi.org/10.1186/s12575-024-00252-3
2. Haj-Hosseini, N., Lindblad, J., Hasséus, B., Kumar, V.V., Subramaniam, N. and Hirsch, J.M., 2024. Early detection of oral potentially malignant disorders: a review on prospective screening methods about global challenges. Journal of Maxillofacial and Oral Surgery, 23(1), pp.23-32. https://doi.org/10.1007/s12663-022-01710-9
3. Chan, A.K.Y., Chu, C.H., Ogawa, H. and Lai, E.H.H., 2024. Improving the oral health of older adults for healthy aging. Journal of Dental Sciences, 19(1), pp.1-7. https://doi.org/10.1016/j.jds.2023.10.018
4. Jagadeesan, D., Sathasivam, K.V., Fuloria, N.K., Balakrishnan, V., Khor, G.H., Ravichandran, M., Solyappan, M., Fuloria, S., Gupta, G., Ahlawat, A. and Yadav, G., 2024. Comprehensive insights into oral squamous cell carcinoma: diagnosis, pathogenesis, and therapeutic advances. Pathology-Research and Practice, p.155489. https://doi.org/10.1016/j.prp.2024.155489
5. Ishida, N., Osawa, S., Sugiura, K., Takebe, T., Takahashi, K., Asai, Y., Tamura, S., Matsuura, T., Yamade, M., Iwaizumi, M. and Hamaya, Y., 2024. Characteristics of Esophageal Squamous Cell Carcinomas Based on Circumferential Localization. JGH Open, 8(12), p.e70063. https://doi.org/10.1002/jgh3.70063
6. Romanò, R., De Felice, F., Ferri, A., Della Monaca, M., Maroldi, R., Licitra, L., Locati, L.D. and Alfieri, S., 2024. Adenoid Cystic carcinoma of minor salivary glands (AdCCmSG): a multidisciplinary update. Expert Review of Anticancer Therapy, pp.1-14. https://doi.org/10.1080/14737140.2024.2357806
7. Alharbi, I., Salawati, F.K., Alnajjar, S. and Alabbasi, A.K., 2024. Tonsillar Diffuse Large B-Cell Lymphoma of Non-Germinal Center Type With Cluster of Differentiation 5 Positive in a Pediatric Girl. Journal of Medical Cases, 15(11), p.324. https://doi.org/10.14740/jmc4288
8. Silveira, F.M., Schuch, L.F. and Bologna-Molina, R., 2024. Classificatory updates in verrucous and cuniculatum carcinomas: Insights from the 5th edition of WHO-IARC head and neck tumor classification. World Journal of Clinical Oncology, 15(4), p.464. https://doi.org/10.5306/wjco.v15.i4.464
9. Kim, J., Kang, J.H., Noh, M.G., Lee, B., Choi, Y.D., Kim, O.J. and Kim, Y., 2024. New potential diagnostic markers for verrucous hyperplasia and verrucous carcinoma based on RNA-sequencing data. Molecular and Cellular Probes, 77, p.101980. https://doi.org/10.1016/j.mcp.2024.101980
10. Adeghe, E.P., Okolo, C.A. and Ojeyinka, O.T., 2024. Navigating early childhood caries management in children with autism and developmental disorders: A US perspective. International Journal of Biological and Pharmaceutical Sciences Archive, 7(01), pp.129-140. https://doi.org/10.53771/ijbpsa.2024.7.1.0029
11. Huang, Q., Ding, H. and Razmjooy, N., 2024. Oral cancer detection using convolutional neural network optimized by combined seagull optimization algorithm. Biomedical Signal Processing and Control, 87, p.105546. https://doi.org/10.1016/j.bspc.2023.105546
12. Li, L., Pu, C., Tao, J., Zhu, L., Hu, S., Qiao, B., Xing, L., Wei, B., Shi, C., Chen, P. and Zhang, H., 2024. Development of an oral cancer detection system through deep learning. BMC Oral Health, 24(1), pp.1-10. https://doi.org/10.1186/s12903-024-05195-5
13. Raghavendra, S. and MJ, P.K., 2024, July. Empowering Diagnosis Techniques: Deep Learning in Oral Cancer Detection. In 2024 Second International Conference on Advances in Information Technology (ICAIT) (Vol. 1, pp. 1-7). IEEE. https://doi.org/10.1109/ICAIT61638.2024.10690417
14. Zhang, L., Shi, R. and Youssefi, N., 2024. Oral Cancer Diagnosis based on Gated Recurrent Unit Networks Optimized by an Improved Version of Northern Goshawk Optimization Algorithm. Heliyon.
15. Kumar, K.V., Palakurthy, S., Balijadaddanala, S.H., Pappula, S.R. and Lavudya, A.K., 2024. Early Detection and Diagnosis of Oral Cancer Using Deep Neural Network. Journal of Computer Allied Intelligence (JCAI, ISSN: 2584-2676), 2(2), pp.22-34. https://doi.org/10.69996/jcai.2024008
16. Raj, M.P. and Muneeswari, G., 2024. Intelligent optimal Archimedes shooty tern deep network (OASTDN) for oral squamous cell carcinoma detection and classification in oral cancer. Multimedia Tools and Applications, pp.1-25. https://doi.org/10.1007/s11042-024-19398-z
17. Varalakshmi, D., Tharaheswari, M., Anand, T. and Saravanan, K.M., 2024. Transforming oral cancer care: The promise of deep learning in diagnosis. Oral Oncology Reports, 10, p.100482. https://doi.org/10.1016/j.oor.2024.100482
18. Meer, M., Khan, M.A., Jabeen, K., Alzahrani, A.I., Alalwan, N., Shabaz, M. and Khan, F., 2025. Deep convolutional neural networks information fusion and improved whale optimization algorithm based smart oral squamous cell carcinoma classification framework using histopathological images. Expert Systems, 42(1), p.e13536. https://doi.org/10.1111/exsy.13536
19. Xu, L., Qiu, K., Li, K., Ying, G., Huang, X. and Zhu, X., 2024. Automatic segmentation of ameloblastoma on CT images using deep learning with limited data. BMC Oral Health, 24(1), p.55.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
