Research Article
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Examination of ResNet and DenseNet Architectures in Early Diagnosis of Oral Cancer: An Evaluation

Year 2025, Volume: 38 Issue: 2, 1006 - 1019, 01.06.2025

Betül Süren , Mutlu Akar

https://doi.org/10.35378/gujs.1480477

Abstract

Oral cancer holds a significant position among head and neck cancers and is encountered quite frequently. Oral cancer, which is the eleventh most common type of cancer worldwide, causes approximately 177,000 deaths and 350,000 new cases every year [1, 2]. The most commonly observed type of oral cancer is Oral Squamous Cell Carcinoma (OSCC) [6] which comprises about 90% of the cases [7]. The survival rate for OSCC is low due to the frequent late diagnosis [19]. This also underscores the importance of early diagnosis. Convolutional neural networks (CNN) are highly preferred for their high performance in early diagnosis. In this study, the early diagnosis of oral cancer has been investigated through the utilization of CNN. Additionally, two models are selected for each of the two different CNN architectures. Classification is carried out with varying hyperparameters on these four models, and the resulting classification accuracies were examined. Furthermore, the two architectures are compared in terms of their performance, highlighting the differences in accuracy and efficiency. The accuracy values for the DenseNet and ResNet architectures in this classification problem are investigated. Models were selected with varying layer depths within each architecture to understand how the number of layers affected classification accuracy. Furthermore, these processes are carried out with different optimizers and epoch numbers, aiming to explore the influence of optimizer choices and varying epoch numbers on classification accuracy. As a result of the study, the highest accuracy rate was measured as 97.01%, achieved using the DenseNet201 architecture with the SGD optimizer.

Keywords

Oral cancer Deep learning ResNet DenseNet

Project Number

FYL-2024- 6104

References

  • [1] Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A. “Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries”, CA: a cancer journal for clinicians, 68(6): 394-424, (2018). DOI: 10.3322/caac.21492
  • [2] Kalavrezos, N., Scully, C. “Mouth cancer for clinicians part 2: epidemiology”, Dental update, 42(4): 354-359, (2015). DOI: 10.12968/denu.2015.42.4.354
  • [3] Sankaranarayanan, R., Ramadas, K., Amarasinghe, H., Subramanian, S., Johnson, N. “Oral cancer: prevention, early detection, and treatment”, Cancer: disease control priorities, third edition, 3: (2015). DOI: 10.1596/978-1-4648-0349-9_ch5
  • [4] Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A., Bray, F. “Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries”, CA: a cancer journal for clinicians, 71(3): 209-249, (2021). DOI: 10.3322/caac.21660
  • [5] World Health Organization. Oral Health. (2018). Available online: https://gco.iarc.fr/today/data/factsheets/cancers/1-Lip-oral-cavity-fact-sheet.pdf accessed on 11 March (2020). Access: https://gco.iarc.fr/today/en
  • [6] Torres-Rosas, R., Torres-Gómez, N., Hernández-Juárez, J., Pérez-Cervera, Y., Hernández-Antonio, A., Argueta-Figueroa, L. “Reported epidemiology of cancer of the lip, oral cavity and oropharynx in Mexico”, Revista Medica del Instituto Mexicano del Seguro Social, 58(4): 494-507, (2020). DOI: 10.24875/rmimss.m20000075
  • [7] Warnakulasuriya, S. “Global epidemiology of oral and oropharyngeal cancer”, Oral oncology, 45(4-5): 309-316, (2009). DOI: 10.1016/j.oraloncology.2008.06.002
  • [8] Speight, P.M., Farthing, P.M. “The pathology of oral cancer”, British dental journal, 225(9): 841-847, (2018). DOI: 10.1038/sj.bdj.2018.926
  • [9] Chow, L.Q.M. “Head and neck cancer. Reply”, The New England journal of medicine, 382(20): e57, (2020). DOI: 10.1056/NEJMc2001370
  • [10] Wong, T.S.C., Wiesenfeld, D. “Oral cancer”, Australian dental journal, 63: S91-S99, (2018). DOI: 10.1111/adj.12594
  • [11] Joseph, B.K. “Oral cancer: prevention and detection”, Medical Principles and Practice, 11(Suppl. 1): 32-35, (2002). DOI: 10.1159/000057776
  • [12] Su, Y.F., Chen, Y.J., Tsai, F.T., Li, W.C., Hsu, M.L., Wang, D.H., Yang, C.C. “Current insights into oral cancer diagnostics”, Diagnostics, 11(7): 1287, (2021). DOI: 10.3390/diagnostics11071287
  • [13] Chattopadhyay, I., Verma, M., Panda, M. “Role of oral microbiome signatures in diagnosis and prognosis of oral cancer”, Technology in cancer research & treatment, 18: 1533033819867354, (2019). DOI: 10.1177/1533033819867354
  • [14] Trimarchi, M., Bertazzoni, G., Bussi, M. “Cocaine induced midline destructive lesions”, Rhinology, 52(2): 104—111, (2014). DOI: 10.4193/Rhino13.112
  • [15] Trimarchi, M., Bellini, C., Fabiano, B., Gerevini, S., Bussi, M. “Multiple mucosal involvement in cicatricial pemphigoid”, Acta Otorhinolaryngologica Italica, 29(4): 222, (2009).
  • [16] Biafora, M., Bertazzoni, G., Trimarchi, M. “Maxillary sinusitis caused by dental implants extending into the maxillary sinus and the nasal cavities”, Journal of Prosthodontics, 23(3): 227-231, (2014). DOI: 10.1111/jopr.12123
  • [17] Trimarchi, M., Bondi, S., Della Torre, E., Terreni, M.R., Bussi, M. “Palate perforation differentiates cocaine-induced midline destructive lesions from granulomatosis with polyangiitis”, Acta Otorhinolaryngologica Italica, 37(4): 281, (2017). DOI: 10.14639/0392-100X-1586
  • [18] Lanzillotta, M., Campochiaro, C., Trimarchi, M., Arrigoni, G., Gerevini, S., Milani, R., Bozzolo, E., Biafora, M., Venturini, E., Cicalese, M.P., Shone, J.H., Sabbadini M.G., Della-Torre, E. “Deconstructing IgG4-related disease involvement of midline structures: Comparison to common mimickers”, Modern Rheumatology, 27(4): 638-645, (2017). DOI: 10.1080/14397595.2016.1227026
  • [19] Sosiawan, A., Chusnita, R., Laksanti, P.A.M., Anwar, A.A., Ramadhani, N.F., Nugraha, A.P. “Utilization of artificial intelligence-assisted histopathological detection in surveillance of oral squamous cell carcinoma staging: A narrative review”, World Journal of Advanced Research and Review, 16(3): 54-59, (2022). Access: https://repository.unair.ac.id/119228/
  • [20] Chinn, S.B., Myers, J.N. “Oral cavity carcinoma: current management, controversies, and future directions”, Journal of clinical oncology, 33(29): 3269-3276, (2015). DOI: 10.1200/JCO.2015.61.2929
  • [21] Silverman, S., Kerr, A.R., Epstein, J.B. “Oral and pharyngeal cancer control and early detection”, Journal of Cancer Education, 25: 279-281, (2010). DOI: 10.1007/s13187-010-0045-6
  • [22] McCullough, M.J., Prasad, G., Farah, C.S. “Oral mucosal malignancy and potentially malignant lesions: an update on the epidemiology, risk factors, diagnosis and management”, Australian dental journal 55: 61-65, (2010). DOI: 10.1111/j.1834-7819.2010.01200.x
  • [23] Gómez, I., Seoane, J., Varela-Centelles, P., Diz, P., Takkouche, B. “Is diagnostic delay related to advanced-stage oral cancer? A meta-analysis”, European Journal of Oral Sciences, 117(5): 541-546, (2009). DOI: 10.1111/j.1600-0722.2009.00672.x
  • [24] Hema Shree, K., Ramani, P., Sherlin, H., Sukumaran, G., Jeyaraj, G., Don, K.R., Santhanam, A., Ramasubramanian, A., Sundar, R. “Saliva as a diagnostic tool in oral squamous cell carcinoma-a systematic review with meta analysis”, Pathology & Oncology Research, 25: 447-453, (2019). DOI: 10.1007/s12253-019-00588-2
  • [25] Ulaganathan, G., Niazi, K.T.M., Srinivasan, S., Balaji, V.R., Manikandan, D., Hameed, K.A.S., Banumathi, A. “A clinicopathological study of various oral cancer diagnostic techniques”, Journal of pharmacy & Bioallied Sciences, 9(Suppl 1): S4, (2017). DOI: 10.4103/jpbs.JPBS_110_17
  • [26] Beristain-Colorado, M.P., Castro-Gutiérrez, M.E.M., Torres-Rosas, R., Vargas-Treviño, M., Moreno-Rodríguez, A., Fuentes-Mascorro, G., Argueta-Figueroa, L. “Application of neural networks for the detection of oral cancer: A systematic review”, Dental and Medical Problems, 61(1): 121-128, (2024). DOI: 10.17219/dmp/159871
  • [27] Tanriver, G., Soluk Tekkesin, M., Ergen, O. “Automated detection and classification of oral lesions using deep learning to detect oral potentially malignant disorders”, Cancers, 13(11): 2766, (2021). DOI: 10.3390/cancers13112766
  • [28] Uthoff, R.D. Song, B., Sunny, S., Patrick, S., Suresh, A., Kolur, T., Keerthi, G., Spires, O., Anbarani, A., Wilder-Smith, P., Kuriakose, M.A., Birur P., Liang R. “Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities”, PloS one, 13(12): e0207493, (2018). DOI: 10.1371/journal.pone.0207493
  • [29] Xu, S., Liu, C., Zong, Y., Chen, S., Lu, Y., Yang, L., Ng, E.Y.K., Wang, Y., Wang, Y., Liu, Y., Hu, W., Zhang, C. “An early diagnosis of oral cancer based on three-dimensional convolutional neural networks”, IEEE Access, 7: 158603-158611, (2019). DOI: 10.1109/ACCESS.2019.2950286
  • [30] Gupta, R.K., Kaur, M., Manhas, J. “Tissue level based deep learning framework for early detection of dysplasia in oral squamous epithelium”, Journal of Multimedia Information System, 6(2): 81-86, (2019). DOI: 10.33851/JMIS.2019.6.2.81
  • [31] Jeyaraj, P.R., Samuel Nadar, E.R. “Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm”, Journal of cancer research and clinical oncology, 145: 829-837, (2019). DOI: 10.1007/s00432-018-02834-7
  • [32] Fu, Q., Chen, Y., Li, Z., Jing, Q., Hu, C., Liu, H., Bao, J., Hong, Y., Shi, T., Li, K., Zou, H., Song, Y., Wang, H., Wang, X., Wang, Y., Liu, J., Liu, H., Chen, S., Chen, R., Zhang, M., Zhao, J., Xiang, J., Liu, B., Jia, J., Wu, H., Zhao, Y., Wan, L., Xiong, X. “A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: A retrospective study”, E Clinical Medicine, 27: (2020). DOI: 10.1016/j.eclinm.2020.100558
  • [33] Welikala, R.A., Remagnino, P., Lim, J.H., Chan, C.S., Rajendran, S., Kallarakkal, T.G., Zain, R.B., Jayasinghe, R.D., Rimal, J., Kerr, A.R., Amtha, R., Patil, K., Tilakaratne, W.M., Gibson, J., Cheong, S.C., Ann Barman, S. “Automated detection and classification of oral lesions using deep learning for early detection of oral cancer”, IEEE Access, 8: 132677-132693, (2020). DOI: 10.1109/ACCESS.2020.3010180
  • [34] Das, N., Hussain, E., Mahanta, L.B. “Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional neural network”, Neural Networks, 128: 47-60, (2020). DOI: 10.1016/j.neunet.2020.05.003
  • [35] Chu, C.S., Lee, N.P., Adeoye, J., Thomson, P., Choi, S.W. “Machine learning and treatment outcome prediction for oral cancer”, Journal of Oral Pathology & Medicine, 49(10): 977-985, (2020). DOI: 10.1111/jop.13089
  • [36] Lin, H., Chen, H., Weng, L., Shao, J., Lin, J. “Automatic detection of oral cancer in smartphone-based images using deep learning for early diagnosis”, Journal of Biomedical Optics, 26(8): 086007-086007, (2021). DOI: 10.1117/1.JBO.26.8.086007
  • [37] Alhazmi, A., Alhazmi, Y., Makrami, A., Masmali, A., Salawi, N., Masmali, K., Patil, S. “Application of artificial intelligence and machine learning for prediction of oral cancer risk”, Journal of Oral Pathology & Medicine, 50(5): 444-450, (2021). DOI: 10.1111/jop.13157
  • [38] Shavlokhova, V., Sandhu, S., Flechtenmacher, C., Koveshazi, I., Neumeier, F., Padrón-Laso, V., Jonke, Ž., Saravi, B., Vollmer, M., Vollmer, A., Hoffman, J., Engel, M., Ristov, O., Freudlsperger, C. “Deep learning on oral squamous cell carcinoma ex vivo fluorescent confocal microscopy data: a feasibility study”, Journal of Clinical Medicine, 10(22): 5326, (2021). DOI: 10.3390/jcm10225326
  • [39] Warin, K., Limprasert, W., Suebnukarn, S., Jinaporntham, S., Jantana, P. “Automatic classification and detection of oral cancer in photographic images using deep learning algorithms”, Journal of Oral Pathology & Medicine, 50(9): 911-918, (2021). DOI: 10.1111/jop.13227
  • [40] Jubair, F., Al-karadsheh, O., Malamos, D., Al Mahdi, S., Saad, Y., Hassona, Y. “A novel lightweight deep convolutional neural network for early detection of oral cancer”, Oral Diseases, 28(4): 1123-1130, (2022). DOI: 10.1111/odi.13825
  • [41] Nanditha, B.R., Geetha, A., Chandrashekar, H.S., Dinesh, M.S., Murali, S. “An ensemble deep neural network approach for oral cancer screening”, International Journal of Online and Biomedical Engineering, (2021). DOI: 10.3991/ijoe.v17i02.19207
  • [42] Alkhadar, H., Macluskey, M., White, S., Ellis, I., Gardner, A. “Comparison of machine learning algorithms for the prediction of five-year survival in oral squamous cell carcinoma”, Journal of Oral Pathology & Medicine, 50(4): 378-384, (2021). DOI: 10.1111/jop.13135
  • [43] Rahman, A., Alqahtani, A., Aldhafferi, N., Nasir, M.U., Khan, M.F., Khan, M.A., Mosavi, A. “Histopathologic oral cancer prediction using oral squamous cell carcinoma biopsy empowered with transfer learning”, Sensors, 22(10): 3833, (2022). DOI: 10.3390/s22103833
  • [44] Deif, M.A., Attar, H., Amer, A., Elhaty, I.A., Khosravi, M.R., Solyman, A.A.A. “Diagnosis of oral squamous cell carcinoma using deep neural networks and binary Particle Swarm optimization on histopathological images: an AIoMT approach”, Computational Intelligence and Neuroscience, 2022(1): 6364102, (2022). DOI: 10.1155/2022/6364102
  • [45] Liyanage, V., Tao, M., Park, J.S., Wang, K.N., Azimi, S. “Malignant and non-malignant oral lesions classification and diagnosis with deep neural networks”, Journal of Dentistry, 137: 104657, (2023). DOI: 10.1016/j.jdent.2023.104657
  • [46] Zhou, M., Jie, W., Tang, F., Zhang, S., Mao, Q., Liu, C., Hao, Y. “Deep learning algorithms for classification and detection of recurrent aphthous ulcerations using oral clinical photographic images”, Journal of Dental Sciences, 19(1): 254-260, (2024). DOI: 10.1016/j.jds.2023.04.022
  • [47] Manikandan, J., Krishna, B.V., Varun, N., Vishal, V., Yugant, S. “Automated Framework for Effective Identification of Oral Cancer Using Improved Convolutional Neural Network”, IEEE Xplore, 1-7, (2023). DOI: 10.1109/ICONSTEM56934.2023.10142794
  • [48] Badik, Ş.T., Akar, M. “Machine learning classification models for the patients who have heart failure”, Sigma Journal of Engineering and Natural Sciences, 42(1): 235-244, (2024). DOI: 10.14744/sigma.2023.00095
  • [49] Akar, M., Sirakov, N.M., Mete, M. “Clifford algebra multivectors and kernels for melanoma classification”, Mathematical Methods in the Applied Sciences, 45(7): 4056-4068, (2022). DOI: 10.1002/mma.8034
  • [50] Akar, M., Sirakov, N.M. “Support vector machine skin lesion classification in Clifford algebra subspaces”, Applications of Mathematics, 64(5): 581-598, (2019). DOI: 10.21136/AM.2019.0292-18
  • [51] Available online: https://www.kaggle.com/ashenafifasilkebede/dataset?select=val Access date: 06.01.2022
  • [52] He, K., Zhang, X., Ren, S., Sun, J. “Deep residual learning for image recognition”, IEEE Xplore, 770-778, (2016). DOI: 10.1109/CVPR.2016.90
  • [53] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q. “Densely connected convolutional networks”, IEEE Xplore, 4700-4708, (2017). DOI: 0.1109/CVPR.2017.243

Year 2025, Volume: 38 Issue: 2, 1006 - 1019, 01.06.2025

Betül Süren , Mutlu Akar

https://doi.org/10.35378/gujs.1480477

Abstract

Project Number

FYL-2024- 6104

References

  • [1] Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A. “Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries”, CA: a cancer journal for clinicians, 68(6): 394-424, (2018). DOI: 10.3322/caac.21492
  • [2] Kalavrezos, N., Scully, C. “Mouth cancer for clinicians part 2: epidemiology”, Dental update, 42(4): 354-359, (2015). DOI: 10.12968/denu.2015.42.4.354
  • [3] Sankaranarayanan, R., Ramadas, K., Amarasinghe, H., Subramanian, S., Johnson, N. “Oral cancer: prevention, early detection, and treatment”, Cancer: disease control priorities, third edition, 3: (2015). DOI: 10.1596/978-1-4648-0349-9_ch5
  • [4] Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A., Bray, F. “Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries”, CA: a cancer journal for clinicians, 71(3): 209-249, (2021). DOI: 10.3322/caac.21660
  • [5] World Health Organization. Oral Health. (2018). Available online: https://gco.iarc.fr/today/data/factsheets/cancers/1-Lip-oral-cavity-fact-sheet.pdf accessed on 11 March (2020). Access: https://gco.iarc.fr/today/en
  • [6] Torres-Rosas, R., Torres-Gómez, N., Hernández-Juárez, J., Pérez-Cervera, Y., Hernández-Antonio, A., Argueta-Figueroa, L. “Reported epidemiology of cancer of the lip, oral cavity and oropharynx in Mexico”, Revista Medica del Instituto Mexicano del Seguro Social, 58(4): 494-507, (2020). DOI: 10.24875/rmimss.m20000075
  • [7] Warnakulasuriya, S. “Global epidemiology of oral and oropharyngeal cancer”, Oral oncology, 45(4-5): 309-316, (2009). DOI: 10.1016/j.oraloncology.2008.06.002
  • [8] Speight, P.M., Farthing, P.M. “The pathology of oral cancer”, British dental journal, 225(9): 841-847, (2018). DOI: 10.1038/sj.bdj.2018.926
  • [9] Chow, L.Q.M. “Head and neck cancer. Reply”, The New England journal of medicine, 382(20): e57, (2020). DOI: 10.1056/NEJMc2001370
  • [10] Wong, T.S.C., Wiesenfeld, D. “Oral cancer”, Australian dental journal, 63: S91-S99, (2018). DOI: 10.1111/adj.12594
  • [11] Joseph, B.K. “Oral cancer: prevention and detection”, Medical Principles and Practice, 11(Suppl. 1): 32-35, (2002). DOI: 10.1159/000057776
  • [12] Su, Y.F., Chen, Y.J., Tsai, F.T., Li, W.C., Hsu, M.L., Wang, D.H., Yang, C.C. “Current insights into oral cancer diagnostics”, Diagnostics, 11(7): 1287, (2021). DOI: 10.3390/diagnostics11071287
  • [13] Chattopadhyay, I., Verma, M., Panda, M. “Role of oral microbiome signatures in diagnosis and prognosis of oral cancer”, Technology in cancer research & treatment, 18: 1533033819867354, (2019). DOI: 10.1177/1533033819867354
  • [14] Trimarchi, M., Bertazzoni, G., Bussi, M. “Cocaine induced midline destructive lesions”, Rhinology, 52(2): 104—111, (2014). DOI: 10.4193/Rhino13.112
  • [15] Trimarchi, M., Bellini, C., Fabiano, B., Gerevini, S., Bussi, M. “Multiple mucosal involvement in cicatricial pemphigoid”, Acta Otorhinolaryngologica Italica, 29(4): 222, (2009).
  • [16] Biafora, M., Bertazzoni, G., Trimarchi, M. “Maxillary sinusitis caused by dental implants extending into the maxillary sinus and the nasal cavities”, Journal of Prosthodontics, 23(3): 227-231, (2014). DOI: 10.1111/jopr.12123
  • [17] Trimarchi, M., Bondi, S., Della Torre, E., Terreni, M.R., Bussi, M. “Palate perforation differentiates cocaine-induced midline destructive lesions from granulomatosis with polyangiitis”, Acta Otorhinolaryngologica Italica, 37(4): 281, (2017). DOI: 10.14639/0392-100X-1586
  • [18] Lanzillotta, M., Campochiaro, C., Trimarchi, M., Arrigoni, G., Gerevini, S., Milani, R., Bozzolo, E., Biafora, M., Venturini, E., Cicalese, M.P., Shone, J.H., Sabbadini M.G., Della-Torre, E. “Deconstructing IgG4-related disease involvement of midline structures: Comparison to common mimickers”, Modern Rheumatology, 27(4): 638-645, (2017). DOI: 10.1080/14397595.2016.1227026
  • [19] Sosiawan, A., Chusnita, R., Laksanti, P.A.M., Anwar, A.A., Ramadhani, N.F., Nugraha, A.P. “Utilization of artificial intelligence-assisted histopathological detection in surveillance of oral squamous cell carcinoma staging: A narrative review”, World Journal of Advanced Research and Review, 16(3): 54-59, (2022). Access: https://repository.unair.ac.id/119228/
  • [20] Chinn, S.B., Myers, J.N. “Oral cavity carcinoma: current management, controversies, and future directions”, Journal of clinical oncology, 33(29): 3269-3276, (2015). DOI: 10.1200/JCO.2015.61.2929
  • [21] Silverman, S., Kerr, A.R., Epstein, J.B. “Oral and pharyngeal cancer control and early detection”, Journal of Cancer Education, 25: 279-281, (2010). DOI: 10.1007/s13187-010-0045-6
  • [22] McCullough, M.J., Prasad, G., Farah, C.S. “Oral mucosal malignancy and potentially malignant lesions: an update on the epidemiology, risk factors, diagnosis and management”, Australian dental journal 55: 61-65, (2010). DOI: 10.1111/j.1834-7819.2010.01200.x
  • [23] Gómez, I., Seoane, J., Varela-Centelles, P., Diz, P., Takkouche, B. “Is diagnostic delay related to advanced-stage oral cancer? A meta-analysis”, European Journal of Oral Sciences, 117(5): 541-546, (2009). DOI: 10.1111/j.1600-0722.2009.00672.x
  • [24] Hema Shree, K., Ramani, P., Sherlin, H., Sukumaran, G., Jeyaraj, G., Don, K.R., Santhanam, A., Ramasubramanian, A., Sundar, R. “Saliva as a diagnostic tool in oral squamous cell carcinoma-a systematic review with meta analysis”, Pathology & Oncology Research, 25: 447-453, (2019). DOI: 10.1007/s12253-019-00588-2
  • [25] Ulaganathan, G., Niazi, K.T.M., Srinivasan, S., Balaji, V.R., Manikandan, D., Hameed, K.A.S., Banumathi, A. “A clinicopathological study of various oral cancer diagnostic techniques”, Journal of pharmacy & Bioallied Sciences, 9(Suppl 1): S4, (2017). DOI: 10.4103/jpbs.JPBS_110_17
  • [26] Beristain-Colorado, M.P., Castro-Gutiérrez, M.E.M., Torres-Rosas, R., Vargas-Treviño, M., Moreno-Rodríguez, A., Fuentes-Mascorro, G., Argueta-Figueroa, L. “Application of neural networks for the detection of oral cancer: A systematic review”, Dental and Medical Problems, 61(1): 121-128, (2024). DOI: 10.17219/dmp/159871
  • [27] Tanriver, G., Soluk Tekkesin, M., Ergen, O. “Automated detection and classification of oral lesions using deep learning to detect oral potentially malignant disorders”, Cancers, 13(11): 2766, (2021). DOI: 10.3390/cancers13112766
  • [28] Uthoff, R.D. Song, B., Sunny, S., Patrick, S., Suresh, A., Kolur, T., Keerthi, G., Spires, O., Anbarani, A., Wilder-Smith, P., Kuriakose, M.A., Birur P., Liang R. “Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities”, PloS one, 13(12): e0207493, (2018). DOI: 10.1371/journal.pone.0207493
  • [29] Xu, S., Liu, C., Zong, Y., Chen, S., Lu, Y., Yang, L., Ng, E.Y.K., Wang, Y., Wang, Y., Liu, Y., Hu, W., Zhang, C. “An early diagnosis of oral cancer based on three-dimensional convolutional neural networks”, IEEE Access, 7: 158603-158611, (2019). DOI: 10.1109/ACCESS.2019.2950286
  • [30] Gupta, R.K., Kaur, M., Manhas, J. “Tissue level based deep learning framework for early detection of dysplasia in oral squamous epithelium”, Journal of Multimedia Information System, 6(2): 81-86, (2019). DOI: 10.33851/JMIS.2019.6.2.81
  • [31] Jeyaraj, P.R., Samuel Nadar, E.R. “Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm”, Journal of cancer research and clinical oncology, 145: 829-837, (2019). DOI: 10.1007/s00432-018-02834-7
  • [32] Fu, Q., Chen, Y., Li, Z., Jing, Q., Hu, C., Liu, H., Bao, J., Hong, Y., Shi, T., Li, K., Zou, H., Song, Y., Wang, H., Wang, X., Wang, Y., Liu, J., Liu, H., Chen, S., Chen, R., Zhang, M., Zhao, J., Xiang, J., Liu, B., Jia, J., Wu, H., Zhao, Y., Wan, L., Xiong, X. “A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: A retrospective study”, E Clinical Medicine, 27: (2020). DOI: 10.1016/j.eclinm.2020.100558
  • [33] Welikala, R.A., Remagnino, P., Lim, J.H., Chan, C.S., Rajendran, S., Kallarakkal, T.G., Zain, R.B., Jayasinghe, R.D., Rimal, J., Kerr, A.R., Amtha, R., Patil, K., Tilakaratne, W.M., Gibson, J., Cheong, S.C., Ann Barman, S. “Automated detection and classification of oral lesions using deep learning for early detection of oral cancer”, IEEE Access, 8: 132677-132693, (2020). DOI: 10.1109/ACCESS.2020.3010180
  • [34] Das, N., Hussain, E., Mahanta, L.B. “Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional neural network”, Neural Networks, 128: 47-60, (2020). DOI: 10.1016/j.neunet.2020.05.003
  • [35] Chu, C.S., Lee, N.P., Adeoye, J., Thomson, P., Choi, S.W. “Machine learning and treatment outcome prediction for oral cancer”, Journal of Oral Pathology & Medicine, 49(10): 977-985, (2020). DOI: 10.1111/jop.13089
  • [36] Lin, H., Chen, H., Weng, L., Shao, J., Lin, J. “Automatic detection of oral cancer in smartphone-based images using deep learning for early diagnosis”, Journal of Biomedical Optics, 26(8): 086007-086007, (2021). DOI: 10.1117/1.JBO.26.8.086007
  • [37] Alhazmi, A., Alhazmi, Y., Makrami, A., Masmali, A., Salawi, N., Masmali, K., Patil, S. “Application of artificial intelligence and machine learning for prediction of oral cancer risk”, Journal of Oral Pathology & Medicine, 50(5): 444-450, (2021). DOI: 10.1111/jop.13157
  • [38] Shavlokhova, V., Sandhu, S., Flechtenmacher, C., Koveshazi, I., Neumeier, F., Padrón-Laso, V., Jonke, Ž., Saravi, B., Vollmer, M., Vollmer, A., Hoffman, J., Engel, M., Ristov, O., Freudlsperger, C. “Deep learning on oral squamous cell carcinoma ex vivo fluorescent confocal microscopy data: a feasibility study”, Journal of Clinical Medicine, 10(22): 5326, (2021). DOI: 10.3390/jcm10225326
  • [39] Warin, K., Limprasert, W., Suebnukarn, S., Jinaporntham, S., Jantana, P. “Automatic classification and detection of oral cancer in photographic images using deep learning algorithms”, Journal of Oral Pathology & Medicine, 50(9): 911-918, (2021). DOI: 10.1111/jop.13227
  • [40] Jubair, F., Al-karadsheh, O., Malamos, D., Al Mahdi, S., Saad, Y., Hassona, Y. “A novel lightweight deep convolutional neural network for early detection of oral cancer”, Oral Diseases, 28(4): 1123-1130, (2022). DOI: 10.1111/odi.13825
  • [41] Nanditha, B.R., Geetha, A., Chandrashekar, H.S., Dinesh, M.S., Murali, S. “An ensemble deep neural network approach for oral cancer screening”, International Journal of Online and Biomedical Engineering, (2021). DOI: 10.3991/ijoe.v17i02.19207
  • [42] Alkhadar, H., Macluskey, M., White, S., Ellis, I., Gardner, A. “Comparison of machine learning algorithms for the prediction of five-year survival in oral squamous cell carcinoma”, Journal of Oral Pathology & Medicine, 50(4): 378-384, (2021). DOI: 10.1111/jop.13135
  • [43] Rahman, A., Alqahtani, A., Aldhafferi, N., Nasir, M.U., Khan, M.F., Khan, M.A., Mosavi, A. “Histopathologic oral cancer prediction using oral squamous cell carcinoma biopsy empowered with transfer learning”, Sensors, 22(10): 3833, (2022). DOI: 10.3390/s22103833
  • [44] Deif, M.A., Attar, H., Amer, A., Elhaty, I.A., Khosravi, M.R., Solyman, A.A.A. “Diagnosis of oral squamous cell carcinoma using deep neural networks and binary Particle Swarm optimization on histopathological images: an AIoMT approach”, Computational Intelligence and Neuroscience, 2022(1): 6364102, (2022). DOI: 10.1155/2022/6364102
  • [45] Liyanage, V., Tao, M., Park, J.S., Wang, K.N., Azimi, S. “Malignant and non-malignant oral lesions classification and diagnosis with deep neural networks”, Journal of Dentistry, 137: 104657, (2023). DOI: 10.1016/j.jdent.2023.104657
  • [46] Zhou, M., Jie, W., Tang, F., Zhang, S., Mao, Q., Liu, C., Hao, Y. “Deep learning algorithms for classification and detection of recurrent aphthous ulcerations using oral clinical photographic images”, Journal of Dental Sciences, 19(1): 254-260, (2024). DOI: 10.1016/j.jds.2023.04.022
  • [47] Manikandan, J., Krishna, B.V., Varun, N., Vishal, V., Yugant, S. “Automated Framework for Effective Identification of Oral Cancer Using Improved Convolutional Neural Network”, IEEE Xplore, 1-7, (2023). DOI: 10.1109/ICONSTEM56934.2023.10142794
  • [48] Badik, Ş.T., Akar, M. “Machine learning classification models for the patients who have heart failure”, Sigma Journal of Engineering and Natural Sciences, 42(1): 235-244, (2024). DOI: 10.14744/sigma.2023.00095
  • [49] Akar, M., Sirakov, N.M., Mete, M. “Clifford algebra multivectors and kernels for melanoma classification”, Mathematical Methods in the Applied Sciences, 45(7): 4056-4068, (2022). DOI: 10.1002/mma.8034
  • [50] Akar, M., Sirakov, N.M. “Support vector machine skin lesion classification in Clifford algebra subspaces”, Applications of Mathematics, 64(5): 581-598, (2019). DOI: 10.21136/AM.2019.0292-18
  • [51] Available online: https://www.kaggle.com/ashenafifasilkebede/dataset?select=val Access date: 06.01.2022
  • [52] He, K., Zhang, X., Ren, S., Sun, J. “Deep residual learning for image recognition”, IEEE Xplore, 770-778, (2016). DOI: 10.1109/CVPR.2016.90
  • [53] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q. “Densely connected convolutional networks”, IEEE Xplore, 4700-4708, (2017). DOI: 0.1109/CVPR.2017.243
There are 53 citations in total.

Details

Primary Language English
Subjects Biostatistics, Biological Mathematics, Applied Mathematics (Other)
Journal Section Statistics
Authors

Betül Süren 0009-0001-4397-0942

Mutlu Akar 0000-0003-3718-7449

Project Number FYL-2024- 6104
Early Pub Date April 28, 2025
Publication Date June 1, 2025
Submission Date May 8, 2024
Acceptance Date January 4, 2025
Published in Issue Year 2025 Volume: 38 Issue: 2

Cite

APA Süren, B., & Akar, M. (2025). Examination of ResNet and DenseNet Architectures in Early Diagnosis of Oral Cancer: An Evaluation. Gazi University Journal of Science, 38(2), 1006-1019. https://doi.org/10.35378/gujs.1480477
AMA Süren B, Akar M. Examination of ResNet and DenseNet Architectures in Early Diagnosis of Oral Cancer: An Evaluation. Gazi University Journal of Science. June 2025;38(2):1006-1019. doi:10.35378/gujs.1480477
Chicago Süren, Betül, and Mutlu Akar. “Examination of ResNet and DenseNet Architectures in Early Diagnosis of Oral Cancer: An Evaluation”. Gazi University Journal of Science 38, no. 2 (June 2025): 1006-19. https://doi.org/10.35378/gujs.1480477.
EndNote Süren B, Akar M (June 1, 2025) Examination of ResNet and DenseNet Architectures in Early Diagnosis of Oral Cancer: An Evaluation. Gazi University Journal of Science 38 2 1006–1019.
IEEE B. Süren and M. Akar, “Examination of ResNet and DenseNet Architectures in Early Diagnosis of Oral Cancer: An Evaluation”, Gazi University Journal of Science, vol. 38, no. 2, pp. 1006–1019, 2025, doi: 10.35378/gujs.1480477.
ISNAD Süren, Betül - Akar, Mutlu. “Examination of ResNet and DenseNet Architectures in Early Diagnosis of Oral Cancer: An Evaluation”. Gazi University Journal of Science 38/2 (June 2025), 1006-1019. https://doi.org/10.35378/gujs.1480477.
JAMA Süren B, Akar M. Examination of ResNet and DenseNet Architectures in Early Diagnosis of Oral Cancer: An Evaluation. Gazi University Journal of Science. 2025;38:1006–1019.
MLA Süren, Betül and Mutlu Akar. “Examination of ResNet and DenseNet Architectures in Early Diagnosis of Oral Cancer: An Evaluation”. Gazi University Journal of Science, vol. 38, no. 2, 2025, pp. 1006-19, doi:10.35378/gujs.1480477.
Vancouver Süren B, Akar M. Examination of ResNet and DenseNet Architectures in Early Diagnosis of Oral Cancer: An Evaluation. Gazi University Journal of Science. 2025;38(2):1006-19.