Araştırma Makalesi
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Integrating Pretrained Deep Neural Networks with Traditional Classification Techniques for Enhanced Oral Cancer Diagnosis

Yıl 2025, Cilt: 13 Sayı: 1, 26 - 36, 30.06.2025

Bilal Şenol , Uğur Demiroğlu

https://doi.org/10.18586/msufbd.1631254

Öz

The purpose of this research is to present a hybrid approach to the classification of oral cancer images. This approach combines traditional classification methods such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Decision Trees with advanced feature extraction from pretrained deep neural networks (GoogleNet and MobileNetV2). Through the use of the suggested method, features are extracted from the deep learning models, resulting in the formation of a robust hybrid model that enhances diagnostic accuracy. The hybrid model achieves a classification accuracy of 90.01% with Quadratic SVM, which represents a 22.36% improvement over solo deep learning models. Comparative analyses indicate the tremendous performance advantages that the hybrid model has achieved. The findings highlight the potential of merging contemporary deep learning skills with older methods in order to improve the accuracy and dependability of medical picture categorization, particularly in the diagnostic process for oral cancer.

Anahtar Kelimeler

Oral Cancer Image Classification Hybrid Model GoogleNet MobileNet-v2

Kaynakça

  • [1] Shigeishi H. Association between human papillomavirus and oral cancer: a literature review, International Journal of Clinical Oncology. 28:8 982-989, 2023.
  • [2] Farsi S., Gardner J.R., King D., Sunde J., Moreno M., Vural E. Head and neck cancer surveillance: The value of computed tomography and clinical exam, American Journal of Otolaryngology. 45:6 104469, 2024.
  • [3] García-Pola M., Pons-Fuster E., Suárez-Fernández C., Seoane-Romero J., Romero-Méndez A., López-Jornet P. Role of artificial intelligence in the early diagnosis of oral cancer. A scoping review, Cancers. 13:18 4600, 2021.
  • [4] Elmaghraby D.A., Alshalla A.A., Alyahyan A., Altaweel M., Al ben Hamad A.M., Alhunfoosh K.M., Albahrani M.A. Public knowledge, practice, and attitude regarding cancer screening: a community-based study in Saudi Arabia, International Journal of Environmental Research and Public Health. 20:2 1114, 2023.
  • [5] Nagao T., Warnakulasuriya S. Screening for oral cancer: Future prospects, research and policy development for Asia, Oral Oncology. 105 104632, 2020.
  • [6] Borse V., Konwar A.N., Buragohain P. Oral cancer diagnosis and perspectives in India, Sensors International. 1 100046, 2020.
  • [7] Hunter B., Hindocha S., Lee R.W. The role of artificial intelligence in early cancer diagnosis, Cancers. 14:6 1524, 2022.
  • [8] Cai L., Gao J., Zhao D. A review of the application of deep learning in medical image classification and segmentation, Annals of Translational Medicine. 8:11, 2020.
  • [9] Yu H., Yang L.T., Zhang Q., Armstrong D., Deen M.J. Convolutional neural networks for medical image analysis: state-of-the-art, comparisons, improvement and perspectives, Neurocomputing. 444 92-110, 2021.
  • [10] Sugeno A., Ishikawa Y., Ohshima T., Muramatsu R. Simple methods for the lesion detection and severity grading of diabetic retinopathy by image processing and transfer learning, Computers in Biology and Medicine. 137 104795, 2021.
  • [11] Muhammad K., Khan S., Del Ser J., De Albuquerque V.H.C. Deep learning for multigrade brain tumor classification in smart healthcare systems: A prospective survey, IEEE Transactions on Neural Networks and Learning Systems. 32:2 507-522, 2020.
  • [12] Waring J., Lindvall C., Umeton R. Automated machine learning: Review of the state-of-the-art and opportunities for healthcare, Artificial Intelligence in Medicine. 104 101822, 2020.
  • [13] Himeur Y., Elnour M., Fadli F., Meskin N., Petri I., Rezgui Y., Amira A. AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives, Artificial Intelligence Review. 56:6 4929-5021, 2023.
  • [14] Wambugu N., Chen Y., Xiao Z., Tan K., Wei M., Liu X., Li J. Hyperspectral image classification on insufficient-sample and feature learning using deep neural networks: A review, International Journal of Applied Earth Observation and Geoinformation. 105 102603, 2021.
  • [15] Warin K., Limprasert W., Suebnukarn S., Jinaporntham S., Jantana P., Vicharueang S. AI-based analysis of oral lesions using novel deep convolutional neural networks for early detection of oral cancer, PLOS One. 17:8 e0273508, 2022.
  • [16] Chillakuru P., Madiajagan M., Prashanth K.V., Ambala S., Shaker Reddy P.C., Pavan J. Enhancing wind power monitoring through motion deblurring with modified GoogleNet algorithm, Soft Computing. 1-11, 2023.
  • [17] Dong K., Zhou C., Ruan Y., Li Y. MobileNetV2 model for image classification, 2nd International Conference on Information Technology and Computer Application (ITCA), IEEE. 476-480, 2020.
  • [18] Ren R., Luo H., Su C., Yao Y., Liao W. Machine learning in dental, oral and craniofacial imaging: a review of recent progress, PeerJ. 9 e11451, 2021.
  • [19] 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, 2021.
  • [20] García-Pola M., Pons-Fuster E., Suárez-Fernández C., Seoane-Romero J., Romero-Méndez A., López-Jornet P. Role of artificial intelligence in the early diagnosis of oral cancer. A scoping review, Cancers. 13:18 4600, 2021.
  • [21] Zhang H., Li W., Zhang H. An Image Recognition Framework for Oral Cancer Cells, Journal of Healthcare Engineering. 2021:1 2449128, 2021.
  • [22] Ferro A., Kotecha S., Fan K. Machine learning in point-of-care automated classification of oral potentially malignant and malignant disorders: a systematic review and meta-analysis, Scientific Reports. 12:1 13797, 2022.
  • [23] Bechelli S. Computer-aided cancer diagnosis via machine learning and deep learning: a comparative review, arXiv preprint arXiv:2210.11943, 2022.
  • [24] Albalawi E., Thakur A., Ramakrishna M.T., Bhatia Khan S., SankaraNarayanan S., Almarri B., Hadi T.H. Oral squamous cell carcinoma detection using EfficientNet on histopathological images, Frontiers in Medicine. 10 1349336, 2024.
  • [25] Warin K., Suebnukarn S. Deep learning in oral cancer—a systematic review, BMC Oral Health. 24:1 212, 2024.
  • [26] https://www.kaggle.com/datasets/fatmaahmedabdallah /oral-cancer-dataset, Last Accessed on 30.10.2024

Gelişmiş Ağız Kanseri Tanısı İçin Önceden Eğitilmiş Derin Sinir Ağlarının Geleneksel Sınıflandırma Teknikleriyle Entegre Edilmesi

Yıl 2025, Cilt: 13 Sayı: 1, 26 - 36, 30.06.2025

Bilal Şenol , Uğur Demiroğlu

https://doi.org/10.18586/msufbd.1631254

Öz

Bu araştırmanın amacı, ağız kanseri görüntülerinin sınıflandırılmasına yönelik hibrit bir yaklaşım sunmaktır. Bu yaklaşım, Destek Vektör Makineleri (SVM), K-En Yakın Komşular (KNN) ve Karar Ağaçları gibi geleneksel sınıflandırma yöntemlerini, önceden eğitilmiş derin sinir ağlarından (GoogleNet ve MobileNetV2) gelişmiş özellik çıkarma ile birleştirir. Önerilen yöntemin kullanımıyla, özellikler derin öğrenme modellerinden çıkarılır ve bu da tanı doğruluğunu artıran sağlam bir hibrit modelin oluşturulmasıyla sonuçlanır. Hibrit model, Kuadratik SVM ile %90,01'lik bir sınıflandırma doğruluğuna ulaşır; bu da tekil derin öğrenme modellerine göre %22,36'lık bir iyileştirmeyi temsil eder. Karşılaştırmalı analizler, hibrit modelin elde ettiği muazzam performans avantajlarını göstermektedir. Bulgular, özellikle ağız kanseri için tanı sürecinde tıbbi resim kategorizasyonunun doğruluğunu ve güvenilirliğini artırmak için çağdaş derin öğrenme becerilerinin eski yöntemlerle birleştirilmesinin potansiyelini vurgulamaktadır.

Anahtar Kelimeler

Ağız Kanseri Görüntü Sınıflandırma Hibrit Model GoogleNet MobileNet-v2

Kaynakça

  • [1] Shigeishi H. Association between human papillomavirus and oral cancer: a literature review, International Journal of Clinical Oncology. 28:8 982-989, 2023.
  • [2] Farsi S., Gardner J.R., King D., Sunde J., Moreno M., Vural E. Head and neck cancer surveillance: The value of computed tomography and clinical exam, American Journal of Otolaryngology. 45:6 104469, 2024.
  • [3] García-Pola M., Pons-Fuster E., Suárez-Fernández C., Seoane-Romero J., Romero-Méndez A., López-Jornet P. Role of artificial intelligence in the early diagnosis of oral cancer. A scoping review, Cancers. 13:18 4600, 2021.
  • [4] Elmaghraby D.A., Alshalla A.A., Alyahyan A., Altaweel M., Al ben Hamad A.M., Alhunfoosh K.M., Albahrani M.A. Public knowledge, practice, and attitude regarding cancer screening: a community-based study in Saudi Arabia, International Journal of Environmental Research and Public Health. 20:2 1114, 2023.
  • [5] Nagao T., Warnakulasuriya S. Screening for oral cancer: Future prospects, research and policy development for Asia, Oral Oncology. 105 104632, 2020.
  • [6] Borse V., Konwar A.N., Buragohain P. Oral cancer diagnosis and perspectives in India, Sensors International. 1 100046, 2020.
  • [7] Hunter B., Hindocha S., Lee R.W. The role of artificial intelligence in early cancer diagnosis, Cancers. 14:6 1524, 2022.
  • [8] Cai L., Gao J., Zhao D. A review of the application of deep learning in medical image classification and segmentation, Annals of Translational Medicine. 8:11, 2020.
  • [9] Yu H., Yang L.T., Zhang Q., Armstrong D., Deen M.J. Convolutional neural networks for medical image analysis: state-of-the-art, comparisons, improvement and perspectives, Neurocomputing. 444 92-110, 2021.
  • [10] Sugeno A., Ishikawa Y., Ohshima T., Muramatsu R. Simple methods for the lesion detection and severity grading of diabetic retinopathy by image processing and transfer learning, Computers in Biology and Medicine. 137 104795, 2021.
  • [11] Muhammad K., Khan S., Del Ser J., De Albuquerque V.H.C. Deep learning for multigrade brain tumor classification in smart healthcare systems: A prospective survey, IEEE Transactions on Neural Networks and Learning Systems. 32:2 507-522, 2020.
  • [12] Waring J., Lindvall C., Umeton R. Automated machine learning: Review of the state-of-the-art and opportunities for healthcare, Artificial Intelligence in Medicine. 104 101822, 2020.
  • [13] Himeur Y., Elnour M., Fadli F., Meskin N., Petri I., Rezgui Y., Amira A. AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives, Artificial Intelligence Review. 56:6 4929-5021, 2023.
  • [14] Wambugu N., Chen Y., Xiao Z., Tan K., Wei M., Liu X., Li J. Hyperspectral image classification on insufficient-sample and feature learning using deep neural networks: A review, International Journal of Applied Earth Observation and Geoinformation. 105 102603, 2021.
  • [15] Warin K., Limprasert W., Suebnukarn S., Jinaporntham S., Jantana P., Vicharueang S. AI-based analysis of oral lesions using novel deep convolutional neural networks for early detection of oral cancer, PLOS One. 17:8 e0273508, 2022.
  • [16] Chillakuru P., Madiajagan M., Prashanth K.V., Ambala S., Shaker Reddy P.C., Pavan J. Enhancing wind power monitoring through motion deblurring with modified GoogleNet algorithm, Soft Computing. 1-11, 2023.
  • [17] Dong K., Zhou C., Ruan Y., Li Y. MobileNetV2 model for image classification, 2nd International Conference on Information Technology and Computer Application (ITCA), IEEE. 476-480, 2020.
  • [18] Ren R., Luo H., Su C., Yao Y., Liao W. Machine learning in dental, oral and craniofacial imaging: a review of recent progress, PeerJ. 9 e11451, 2021.
  • [19] 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, 2021.
  • [20] García-Pola M., Pons-Fuster E., Suárez-Fernández C., Seoane-Romero J., Romero-Méndez A., López-Jornet P. Role of artificial intelligence in the early diagnosis of oral cancer. A scoping review, Cancers. 13:18 4600, 2021.
  • [21] Zhang H., Li W., Zhang H. An Image Recognition Framework for Oral Cancer Cells, Journal of Healthcare Engineering. 2021:1 2449128, 2021.
  • [22] Ferro A., Kotecha S., Fan K. Machine learning in point-of-care automated classification of oral potentially malignant and malignant disorders: a systematic review and meta-analysis, Scientific Reports. 12:1 13797, 2022.
  • [23] Bechelli S. Computer-aided cancer diagnosis via machine learning and deep learning: a comparative review, arXiv preprint arXiv:2210.11943, 2022.
  • [24] Albalawi E., Thakur A., Ramakrishna M.T., Bhatia Khan S., SankaraNarayanan S., Almarri B., Hadi T.H. Oral squamous cell carcinoma detection using EfficientNet on histopathological images, Frontiers in Medicine. 10 1349336, 2024.
  • [25] Warin K., Suebnukarn S. Deep learning in oral cancer—a systematic review, BMC Oral Health. 24:1 212, 2024.
  • [26] https://www.kaggle.com/datasets/fatmaahmedabdallah /oral-cancer-dataset, Last Accessed on 30.10.2024
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgi Sistemleri (Diğer), Yazılım Mühendisliği (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Bilal Şenol 0000-0002-3734-8807

Uğur Demiroğlu 0000-0002-0000-8411

Erken Görünüm Tarihi 24 Haziran 2025
Yayımlanma Tarihi 30 Haziran 2025
Gönderilme Tarihi 1 Şubat 2025
Kabul Tarihi 22 Nisan 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 13 Sayı: 1

Kaynak Göster

APA Şenol, B., & Demiroğlu, U. (2025). Integrating Pretrained Deep Neural Networks with Traditional Classification Techniques for Enhanced Oral Cancer Diagnosis. Mus Alparslan University Journal of Science, 13(1), 26-36. https://doi.org/10.18586/msufbd.1631254
AMA Şenol B, Demiroğlu U. Integrating Pretrained Deep Neural Networks with Traditional Classification Techniques for Enhanced Oral Cancer Diagnosis. MAUN Fen Bil. Dergi. Haziran 2025;13(1):26-36. doi:10.18586/msufbd.1631254
Chicago Şenol, Bilal, ve Uğur Demiroğlu. “Integrating Pretrained Deep Neural Networks With Traditional Classification Techniques for Enhanced Oral Cancer Diagnosis”. Mus Alparslan University Journal of Science 13, sy. 1 (Haziran 2025): 26-36. https://doi.org/10.18586/msufbd.1631254.
EndNote Şenol B, Demiroğlu U (01 Haziran 2025) Integrating Pretrained Deep Neural Networks with Traditional Classification Techniques for Enhanced Oral Cancer Diagnosis. Mus Alparslan University Journal of Science 13 1 26–36.
IEEE B. Şenol ve U. Demiroğlu, “Integrating Pretrained Deep Neural Networks with Traditional Classification Techniques for Enhanced Oral Cancer Diagnosis”, MAUN Fen Bil. Dergi., c. 13, sy. 1, ss. 26–36, 2025, doi: 10.18586/msufbd.1631254.
ISNAD Şenol, Bilal - Demiroğlu, Uğur. “Integrating Pretrained Deep Neural Networks With Traditional Classification Techniques for Enhanced Oral Cancer Diagnosis”. Mus Alparslan University Journal of Science 13/1 (Haziran 2025), 26-36. https://doi.org/10.18586/msufbd.1631254.
JAMA Şenol B, Demiroğlu U. Integrating Pretrained Deep Neural Networks with Traditional Classification Techniques for Enhanced Oral Cancer Diagnosis. MAUN Fen Bil. Dergi. 2025;13:26–36.
MLA Şenol, Bilal ve Uğur Demiroğlu. “Integrating Pretrained Deep Neural Networks With Traditional Classification Techniques for Enhanced Oral Cancer Diagnosis”. Mus Alparslan University Journal of Science, c. 13, sy. 1, 2025, ss. 26-36, doi:10.18586/msufbd.1631254.
Vancouver Şenol B, Demiroğlu U. Integrating Pretrained Deep Neural Networks with Traditional Classification Techniques for Enhanced Oral Cancer Diagnosis. MAUN Fen Bil. Dergi. 2025;13(1):26-3.
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