Volume 24 , Issue 4 , PP: 71-81, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Arwa Darwish Alzughaibi 1 , Ebtesam Al-Mansor 2 *
Doi: https://doi.org/10.54216/IJNS.240405
Neutrosophic soft sets (NSS) are highly effective in representing neutral uncertain data. NSS model attracts several authors because it has huge range of applications in several areas such as decision-making, data analysis, smoothness of functions, probability theory, measurement theory, predicting, and operations research. Oral squamous cell carcinoma (OSCC) is the most general tumor around the world and its occurrence is on the increase in several populations. Early diagnosis plays vital role in improving diagnosis, treatment outcomes and survival rates. Although the new developments in understanding molecular mechanisms, late analysis and the implementation of precision medicine for OSCC patients continue to present problems. Early diagnosis and detection can support doctors in offering optimum patient care and effectual treatment. In recent years, the execution of several machine-learning (ML) approaches in cancer analysis has provided valuable insights, facilitating more effective and precise treatment decision-making. Oral Cancer screening can progress with the execution of artificial intelligence (AI) approaches. AI offers support to the oncology region by correctly examining a huge database in many imaging modalities. This article develops a Bat Algorithm with Rough Neutrosophic Soft Set for Oral Cancer Diagnosis (BARNSS-OCD) technique. The main intention of the BARNSS-OCD technique is to exploit deep learning (DL) model for enhanced identification of OC. In the BARNSS-OCD technique, median filtering (MF) is used for image pre-processing and the feature extraction takes place using deep convolutional neural network (DCNN) model. In addition, bat algorithm (BA) is used for the hyperparameter selection of the DCNN model. For OC detection process, the BARNSS-OCD technique applies RNSS model. To exhibit the improved performance of the BARNSS-OCD technique, a sequence of experiments is involved. The simulation outcomes indicate that the BARNSS-OCD technique gains better performance compared to other DL models
Oral Cancer , Neutrosophic Soft Set , Bat Algorithm , Oral Squamous Cell Carcinoma , Deep Learning
[1] Jeyaraj, P.; Nadar, E.S. Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm. J. Cancer Res. Clin. Oncol. 2019, 145, 829–837.
[2] Adeoye, J.; Choi, S.W.; Thomson, P. Bayesian disease mapping and The ‘high-risk’ oral cancer population in Hong Kong. J. Oral Pathol. Med. 2020, 49, 907–913.
[3] R. Saarumathi, W. Ritha. (2024). A Legitimate Productive Repertoire Replica Betwixt Envirotech Outlay Towards Fragile Commodities Using Trapezoidal Neutrosophic Fuzzy Number. International Journal of Neutrosophic Science, 24 ( 1 ), 104-118
[4] Smarandache F., and Abobala, M., " n-Refined Neutrosophic Vector Spaces", International Journal of Neutrosophic Science, Vol. 7, pp. 47-54, 2020.
[5] Chu, C.; Lee, N.; Ho, J.; Choi, S.; Thomson, P. Deep learning for clinical image analyses in oral squamous cell carcinoma: A review. JAMA Otolaryngol. Head Neck Surg. 2021, 147, 893–900.
[6] Tuqa A. H. Al-Tamimi, Luay A. A. Al-Swidi , Ali H. M. Al-Obaidi. "Partner Sets for Generalizations of MultiNeutrosophic Sets." International Journal of Neutrosophic Science, Vol. 24, No. 1, 2024 ,PP. 08-13
[7] Parimala, M., Karthika, M. and Smarandache, F., 2020. A review of fuzzy soft topological spaces, intuitionistic fuzzy soft topological spaces and neutrosophic soft topological spaces. International Journal of Neutrosophic Science, Vol. 10, No. 2, 2020 ,PP. 96-104.
[8] Ashraf, S. and Abdullah, S., 2020. Decision support modeling for agriculture land selection based on sine trigonometric single valued neutrosophic information. International Journal of Neutrosophic Science (IJNS), 9(2), pp.60-73.
[9] Song, B.; Sunny, S.; Li, S.; Gurushanth, K.; Mendonca, P.; Mukhia, N.; Patrick, S.; Gurudath, S.; Raghavan, S.; Tsusennaro, I.; et al. Bayesian deep learning for reliable oral cancer image classification. Biomed. Opt. Express 2021, 12, 6422–6430.
[10] Song, B.; Li, S.; Sunny, S.; Gurushanth, K.; Mendonca, P.; Mukhia, N.; Patrick, S.; Gurudath, S.; Raghavan, S.; Tsusennaro, I.; et al. Classification of imbalanced oral cancer image data from high-risk population. J. Biomed. Opt. 2021, 26, 105001.
[11] Mira, E.S., Sapri, A.M.S., Aljehanı, R.F., Jambı, B.S., Bashir, T., El-Kenawy, E.S.M. and Saber, M., 2024. Early Diagnosis of Oral Cancer Using Image Processing and Artificial Intelligence. Fusion: Practice and Applications, 14(1), pp.293-308.
[12] Mohan, R., Rama, A., Raja, R.K., Shaik, M.R., Khan, M., Shaik, B. and Rajinikanth, V., 2023. OralNet: Fused Optimal Deep Features Framework for Oral Squamous Cell Carcinoma Detection. Biomolecules, 13(7), p.1090.
[13] 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.
[14] Marzouk, R., Alabdulkreem, E., Dhahbi, S., Nour, M.K., Duhayyim, M.A., Othman, M., Hamza, M.A., Motwakel, A., Yaseen, I. and Rizwanullah, M., 2022. Deep Transfer Learning Driven Oral Cancer Detection and Classification Model. Computers, Materials & Continua, 73(2).
[15] 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.
[16] Khan, S.U.R. and Asif, S., 2024. Oral cancer detection using feature-level fusion and novel self-attention mechanisms. Biomedical Signal Processing and Control, 95, p.106437.
[17] Huang, Q., Ding, H. and Razmjooy, N., 2023. Optimal deep learning neural network using ISSA for diagnosing the oral cancer. Biomedical Signal Processing and Control, 84, p.104749.
[18] Jana, B.R., Thotakura, H., Baliyan, A., Sankararao, M., Deshmukh, R.G. and Karanam, S.R., 2023. Pixel density based trimmed median filter for removal of noise from surface image. Applied Nanoscience, 13(2), pp.1017-1028.
[19] Lin, C., Tuo, X., Wu, L., Zhang, G. and Zeng, X., 2024. Accurate Capacity Prediction and Evaluation with Advanced SSA-CNN-BiLSTM Framework for Lithium-Ion Batteries. Batteries, 10(3), p.71.
[20] Olivares, R., Salinas, O., Ravelo, C., Soto, R. and Crawford, B., 2024. Enhancing the Efficiency of a Cyber SOC Using Biomimetic Algorithms Empowered by Deep Q–Learning.
[21] Bhutani, K. and Aggarwal, S., 2017. Neutrosophic Rough Soft Set–A Decision Making Approach to Appendicitis Problem. Neutrosophic sets and systems, 16, pp.70-75.
[22] Alabdan, R., Alruban, A., Hilal, A.M. and Motwakel, A., 2022, December. Artificial-intelligence-based decision making for oral potentially malignant disorder diagnosis in internet of medical things environment. In Healthcare (Vol. 11, No. 1, p. 113). MDPI.
