Automated Lion Optimization Algorithm with Deep Transfer Learning Based Oral Cancer Detection and Classification Model

Article Sidebar

Published: Apr 30, 2025
Keywords:
Oral Cancer Transfer Learning Lion Optimization Algorithm Bilateral Filtering Histopathological Image

Main Article Content

Sathishkumar R*
Research Scholar, Department of Computer Science and Engineering, Annamalai University, Annamalai Nagar, 608002, Chidambaram, Tamil Nadu, India
Govindarajan M
Associate Professor, Department of Computer Science and Engineering, Annamalai University,Annamalai Nagar, 608002, Chidambaram, Tamil Nadu, India

Abstract

Oral cancer (OC) recognition involves leveraging innovative technologies like imaging models and machine learning (ML) techniques to analyze oral cavity anomalies, helping in the initial analysis and enhancing treatment results. These new methods contribute to appropriate intervention and the probable for improved existence rates in individuals in danger of oral cancer. The normal analysis of oral cancer is the microscopic study of specimens detached especially over incisional biopsies of oral mucosa through a clinical spotted suspicious lesion. The use of deep learning (DL) methods is effective in many kinds of cancer; but, a restricted research study has been completed utilizing histopathological OSCC images. Unlike conventional ML, which needs physical feature removal and reflects area expertise, DL can mechanically remove features with an alteration from hand-designed to data-driven features. Despite the customary medical techniques employed in oral classification, automatic models dependent upon a DL framework display promising outcomes. Therefore, this article presents an automated lion optimization algorithm with a deep transfer learning-based oral cancer detection and classification (LOADL-OCDC) methodology. The main intention of the LOADL-OCDC technique is to recognize and categorize the occurrence of oral cancer into distinct classes. The LOADL-OCDC technique follows a multistage process. Initially, the LOADL-OCDC technique performs bilateral filtering-based noise elimination and CLAHE-based contrast improvement. Next, the EfficientNet model can be applied to learn complex and intrinsic feature patterns from the pre-processed images. In the presented LOADL-OCDC technique, the lion optimization algorithm (LOA) can be applied for fine-tuning the hyperparameters of the EfficientNet model. For cancer detection, the LOADL-OCDC technique applies a deep recurrent neural network (DRNN) system. A general experimental study is created to investigate the detection results of the LOADL-OCDC technique. The complete comparison study reported the supremacy of the LOADL-OCDC system in terms of different measures.

Article Details

Issue
Vol. 167 No. A2 (S) (2025): Technologies and Their Effects on Real-Time Social Development
Section
Articles

References

Lin, H., Chen, H., Weng, L., Shao, J. and Lin, J., 2021. Automatic detection of oral cancer in smartphone-based images using deep learning for early diagnosis. Journal of Biomedical Optics, 26(8), pp.086007-086007.

Chang, X., Yu, M., Liu, R., Jing, R., Ding, J., Xia, J., Zhu, Z., Li, X., Yao, Q., Zhu, L. and Zhang, T., 2023. Deep learning methods for oral cancer detection using Raman spectroscopy. Vibrational Spectroscopy, 126, p.103522.

Warin, K., Limprasert, W., Suebnukarn, S., Jinaporntham, S. and Jantana, P., 2021. Automatic classification and detection of oral cancer in photographic images using deep learning algorithms. Journal of Oral Pathology & Medicine, 50(9), pp.911-918.

Shamim, M.Z.M., Syed, S., Shiblee, M., Usman, M., Ali, S.J., Hussein, H.S. and Farrag, M., 2022. Automated detection of oral pre-cancerous tongue lesions using deep learning for early diagnosis of oral cavity cancer. The Computer Journal, 65(1), pp.91-104.

Srinivasa Sai Abhijit Challapalli. 2024. Sentiment Analysis of the Twitter Dataset for the Prediction of Sentiments. Journal of Sensors, IoT & Health Sciences, 2(4), 1-15.

Fu, Q., Chen, Y., Li, Z., Jing, Q., Hu, C., Liu, H., Bao, J., Hong, Y., Shi, T., Li, K. and Zou, H., 2020. A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: A retrospective study. EClinicalMedicine, 27.

Srinivasa Sai Abhijit Challapalli. (2024). Optimizing Dallas-Fort Worth Bus Transportation System Using Any Logic. Journal of Sensors, IoT & Health Sciences, 2(4), 40-55.

Al-Rawi, N., Sultan, A., Rajai, B., Shuaeeb, H., Alnajjar, M., Alketbi, M., Mohammad, Y., Shetty, S.R. and Mashrah, M.A., 2022. The effectiveness of artificial intelligence in the detection of oral cancer. international dental journal, 72(4), pp.436-447.

K. Vinay Kumar, Sumanaswini Palakurthy, Sri Harsha Balijadaddanala, Sharmila Reddy Pappula, & Anil Kumar Lavudya. (2024). Early Detection and Diagnosis of Oral Cancer Using Deep Neural Network. Journal of Computer Allied Intelligence, 2(2), 22-34.

Swapna Saturi, Gundeti Naveen Kumar, Namilla Siri, Kota Madhuri, & Vallapureddy Yashwanth Reddy. 2025. Deep Learning for Understanding Multi label Imbalanced Chest X-rayDatasets. Journal of Computer Allied Intelligence(JCAI, ISSN: 2584-2676), 3(1), 40-47.

Figueroa, K.C., Song, B., Sunny, S., Li, S., Gurushanth, K., Mendonca, P., Mukhia, N., Patrick, S., Gurudath, S., Raghavan, S. and Imchen, T., 2022. Interpretable deep learning approach for oral cancer classification using guided attention inference network. Journal of biomedical optics, 27(1), pp.015001-015001.

Yang, Z., Pan, H., Shang, J., Zhang, J. and Liang, Y., 2023. Deep-Learning-Based Automated Identification and Visualization of Oral Cancer in Optical Coherence Tomography Images. Biomedicines, 11(3), p.802.

Alabi, R.O., Almangush, A., Elmusrati, M. and Mäkitie, A.A., 2022. Deep machine learning for oral cancer: from precise diagnosis to precision medicine. Frontiers in Oral Health, 2, p.794248.

Amin, I., Zamir, H. and Khan, F.F., 2021. Histopathological image analysis for oral squamous cell carcinoma classification using concatenated deep learning models. medRxiv, pp.2021-05.

Ananthakrishnan, B., Shaik, A., Kumar, S., Narendran, S.O., Mattu, K. and Kavitha, M.S., 2023. Automated Detection and Classification of Oral Squamous Cell Carcinoma Using Deep Neural Networks. Diagnostics, 13(5), p.918.

Huang, Q., Ding, H. and Razmjooy, N., 2023. Optimal deep learning neural network using ISSA for diagnosing oral cancer. Biomedical Signal Processing and Control, 84, p.104749.

Al Duhayyim, M., Malibari, A., Dhahbi, S., Nour, M.K., Al-Turaiki, I., Obayya, M.I. and Mohamed, A., 2023. Sailfish Optimization with Deep Learning Based Oral Cancer Classification Model. Comput. Syst. Sci. Eng., 45(1), pp.753-767.

Warin, K., Limprasert, W., Suebnukarn, S., Jinaporntham, S., Jantana, P. and Vicharueang, S., 2022. AI-based analysis of oral lesions using novel deep convolutional neural networks for early detection of oral cancer. Plos one, 17(8), p.e0273508.

Nanditha, B.R., Geetha, A., Chandrashekar, H.S., Dinesh, M.S. and Murali, S., 2021. An ensemble deep neural network approach for oral cancer screening.

Prabhakaran, R. and Mohana, J., 2023. Stochastic gradient descent-based convolutional neural network to detect and classify oral cavity cancer. Soft Computing, 27(13), pp.9169-9178.

Begum, S.H. and Vidyullatha, P., 2023. Deep Learning Model for Automatic Detection of Oral squamous cell carcinoma (OSCC) using Histopathological Images. International Journal of Computing and Digital Systems.

Ravikumar, M., Rachana, P.G., Shivaprasad, B.J. and Guru, D.S., 2021. Enhancement of mammogram images using CLAHE and bilateral filter approaches. In Cybernetics, Cognition and Machine Learning Applications: Proceedings of ICCCMLA 2020 (pp. 261-271). Springer Singapore.

Argho, A.G., Maswood, M.M.S., Mahmood, M.I. and Mondol, N., 2023. EfficientCovNet: A CNN-based approach to detect various pulmonary diseases including COVID-19 using modified EfficientNet. Intelligent Systems with Applications, p.200315.

Priya, R.D., Karthikeyan, S., Indra, J., Kirubashankar, S., Abraham, A., Gabralla, L.A., Sivaraj, R. and Nandhagopal, S.M., 2023. Self-Adaptive Hybridized Lion Optimization Algorithm with Transfer Learning for Ancient Tamil Character Recognition in Stone Inscriptions. IEEE Access.

Xia, M., Zheng, X., Imran, M. and Shoaib, M., 2020. Data-driven prognosis method using hybrid deep recurrent neural network. Applied Soft Computing, 93, p.106351.

Ribeiro-de-Assis, M.C.F., Soares, J.P., de Lima, L.M., de Barros, L.A.P., Grão-Velloso, T.R., Krohling, R.A. and Camisasca, D.R., 2023. NDB-UFES: An oral cancer and leukoplakia dataset composed of histopathological images and patient data. Data in Brief, 48, p.109128.

Das, M., Dash, R. and Mishra, S.K., 2023. Automatic detection of oral squamous cell carcinoma from histopathological images of oral mucosa using deep convolutional neural network. International Journal of Environmental Research and Public Health, 20(3), p.2131.

Sathishkumar, R., & Govindarajan, M. 2023. A Comprehensive Study on Artificial Intelligence Techniques for Oral Cancer Diagnosis: Challenges and Opportunities. In 2023 International Conference on System, Computation, Automation and Networking (ICSCAN) (pp. 1-5). IEEE.

Mira, E. S., Sapri, A. M. S., Aljehanı, R. F., Jambı, B. S., Bashir, T., El-Kenawy, E. S. M., & Saber, M. 2024. Early Diagnosis of Oral Cancer Using Image Processing and Artificial Intelligence. Fusion: Practice and Applications, 14(1), 293-308.

Das, M., Dash, R., & Mishra, S. K. 2023. Automatic detection of oral squamous cell carcinoma from histopathological images of oral mucosa using deep convolutional neural network. International Journal of Environmental Research and Public Health, 20(3), 2131.

Rahman, A. U., Alqahtani, A., Aldhafferi, N., Nasir, M. U., Khan, M. F., Khan, M. A., & Mosavi, A. 2022. Histopathologic oral cancer prediction using oral squamous cell carcinoma biopsy empowered with transfer learning. Sensors, 22(10), 3833.

Tanriver, G., Soluk Tekkesin, M., & Ergen, O. 2021. Automated detection and classification of oral lesions using deep learning to detect oral potentially malignant disorders. Cancers, 13(11), 2766.

Welikala, R. A., Remagnino, P., Lim, J. H., Chan, C. S., Rajendran, S., Kallarakkal, T. G., ... & Barman, S. A. 2020. Automated detection and classification of oral lesions using deep learning for early detection of oral cancer. IEEE Access, 8, 132677-132693.

Panigrahi, S.; Swarnkar, T. Automated Classification of Oral Cancer Histopathology images using Convolutional Neural Network. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); IEEE: New York, NY, USA, 2019; pp. 1232–1234.

Alhazmi, A., Alhazmi, Y., Makrami, A., Masmali, A., Salawi, N., Masmali, K., & Patil, S. 2021. Application of artificial intelligence and machine learning for prediction of oral cancer risk. Journal of Oral Pathology & Medicine, 50(5), 444-450.