Polish Journal of Pathology
2/2025 vol. 76
Original paper
WYCOFANY

Histopathological image analysis and enhanced diagnostic accuracy explainability for oral cancer detection

V.P. Gladis Pushparathi 1
,
Sylaja Vallee NarayanS.R 2
,
Pratheeba R.S. 3
,
V. Naveen 4
  1. Department of Computer Science and Engineering, R.M.K. College of Engineering and Technology, R.S.M. Nagar, Puduvoyal, Thiruvalur Dist, Tamil Nadu, India
  2. Department of Computer Science and Engineering, GITAM University, Bangalore, Karnataka, India
  3. Department of Computer Science and Engineering, Velammal Institute of Technology, Panchetti, Chennai, Tamil Nadu, India
  4. Department of Computer Science and Engineering, Madanapalle Institute of Technology and Science, Madanapalle, Andhra Pradesh, India
Pol JPathol 2025; 76 (2): 120-130
DOI: https://doi.org/10.5114/pjp.2025.153973
Data publikacji online: 2025/09/22
Article file
Histopathological image analysis.pdf
Confronting perimenopausal women’s knowledge of coronary heart disease with their health behaviours. Controversial role of hormone replacement therapy in the protection of coronary heart disease
1. Al-Rawi N, Sultan A, Rajai B, et al. The Effectiveness of Artifi­cial Intelligence in Detection of Oral Cancer. Int Dent J 2022; 72: 436-447.
2. 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 Dis 2022; 28: 1123-1130.
3. Warin K, Suebnukarn S. Deep learning in oral cancer – a sys­tematic review. BMC Oral Health 2024; 24: 212.
4. Weber A, Enderle-Ammour K, Kurowski K, et al. AI-Based Detection of Oral Squamous Cell Carcinoma with Raman His­tology. Cancers (Basel) 2024; 16: 689.
5. Yang SY, Li SH, Liu JL, et al. Histopathology-Based Diagnosis of Oral Squamous Cell Carcinoma Using Deep Learning. J Dent Res 2022; 101: 1321-1327.
6. Camalan S, Mahmood H, Binol H, et al. Convolutional Neu­ral Network-Based Clinical Predictors of Oral Dysplasia: Class Activation Map Analysis of Deep Learning Results. Cancers (Basel) 2021; 13 1291.
7. Musulin J, Štifanić D, Zulijani A, Ćabov T, Dekanić A, Car Z. An Enhanced Histopathology Analysis: An AI-Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue. Cancers (Basel) 2021; 13: 1784.
8. Tanriver G, Soluk Tekkesin M, Ergen O. Automated Detec­tion and Classification of Oral Lesions Using Deep Learning to Detect Oral Potentially Malignant Disorders. Cancers (Basel) 2021; 13: 2766.
9. Deo BS, Pal M, Panigrahi PK, Pradhan A. An ensemble deep learning model with empirical wavelet transform feature for oral cancer histopathological image classification. Int J Data Sci Anal 2024; 20: 1005-1022.
10. 10. Marzouk R, Eatedal A, Sami D, et al. Deep Transfer Learning Driven Oral Cancer Detection and Classification Model. Com­puters, Materials and Continua 2022; 3: 3905-3920.
11. Das M, Dash R, Mishra SK. Automatic Detection of Oral Squamous Cell Carcinoma from Histopathological Images of Oral Mucosa Using Deep Convolutional Neural Network. Int J Environ Res Public Health 2023; 20: 2131.
12. Gomes RFT, Schmith J, Figueiredo RM, et al. Use of Artificial Intelligence in the Classification of Elementary Oral Lesions from Clinical Images. Int J Environ Res Public Health 2023; 20: 3894.
13. Myriam H, Abdelaziz AA, El-Sayed M, et al. Advanced me­ta-heuristic algorithm based on Particle Swarm and Al-biruni Earth Radius optimization methods for oral cancer detection. IEEE Access 2023; 11: 23681-23700.
14. Song B, Kc DR, Yang RY, Li S, Zhang C, Liang R. Classifi­cation of Mobile-Based Oral Cancer Images Using the Vision Transformer and the Swin Transformer. Cancers (Basel) 2024; 16: 987.
15. Confer MP, Falahkheirkhah K, Surendran S, et al. Rapid and Label-Free Histopathology of Oral Lesions Using Deep Learn­ing Applied to Optical and Infrared Spectroscopic Imaging Data. J Pers Med 2024; 14: 304.
16. Oral Cancer Histopathological Image Dataset Link: https:// www.kaggle.com/datasets/ashenafifasilkebede/dataset (Access: Jul 2023).
17. Multi Cancer Dataset Link: https://www.kaggle.com/datasets/ obulisainaren/multi-cancer (Access: Jul 2023).
18. NDB-UFES dataset Link: https://data.mendeley.com/datasets/ bbmmm4wgr8/4 (Access: Jul 2023).
19. Ahmad M, Irfan MA, Sadique U, et al. Multi-Method Analysis of Histopathological Image for Early Diagnosis of Oral Squa­mous Cell Carcinoma Using Deep Learning and Hybrid Tech­niques. Cancers (Basel) 2023; 15: 5247.
20. Redie DK, Bilgaiyan S, Sagnika S. Oral cancer detection using transfer learning-based framework from histopathology imag­es. J Electronic Imaging 2023; 32: 053004-053004.
21. Abbas T, Fatima A, Shahzad T, Alharbi M, Khan MA, Ahmed A. Multidisciplinary cancer disease classification us­ing adaptive FL in healthcare industry 5.0. Sci Rep 2024; 14: 18643.
22. Rajadurai S, Perumal K, Ijaz MF, Chowdhary CL. Precision­LymphoNet: Advancing Malignant Lymphoma Diagnosis via Ensemble Transfer Learning with CNNs. Diagnostics (Basel) 2024; 14: 469.
Copyright: © 2025 Polish Association of Pathologists and the Polish Branch of the International Academy of Pathology This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License (http://creativecommons.org/licenses/by-nc-sa/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material, provided the original work is properly cited and states its license.
Share
without publication fees
Integrated with
Editorial Policies
Sarajevo Declaration on Integrity and Visibility of Scholarly Publications