Review of Classification and Early of Oral and Mouth Diseases by Using Advanced Deep Learning Methods

Abstract

Oral and mouth diseases are among the most prevalent health issues, significantly affecting individuals\' quality of life and posing serious health risks if left undiagnosed. Early detection and accurate diagnosis are criticalinpreventingthe progressionofconditions suchasoral cancer and other potentially malignant disorders. Recent advancements in digital tools and deep learning have opened new avenues for improving diagnostic accuracy and accessibility. This paper tackles these issues by utilizing the InceptionResNetV2 architectureto create a robust andefficient classification system for oral diseases. The system leverages a comprehensive dataset of oral condition images, processed and trained on platforms like Google Colab or Jupyter Notebook to ensure scalability and computational efficiency. The trained model is seamlessly integrated into a Flask-based web application, allowing users touploadimages and receiveprecisediagnosticresults.Bycombiningadvanced deep learning techniques with user-friendly technology, this paper aims to facilitate early detection and diagnosis of oral diseases, enabling timely medical intervention and improving overall oral healthcare outcomes [1].

Introduction

Oral and mouth diseases, ranging from common issues like cavities and gum disease to severe conditions such as oral cancer and potentially malignant disorders (e.g., oral submucous fibrosis), pose serious global health challenges. Early detection and diagnosis are critical to preventing complications, but traditional diagnostic methods are often time-consuming, subjective, and limited by access to skilled professionals, especially in underserved areas.

This study proposes using deep learning—specifically the InceptionResNetV2 architecture—to develop an efficient and accurate classification system for oral diseases. The model is trained on a curated and preprocessed dataset of oral disease images, using techniques like resizing, normalization, and augmentation to improve performance. Training is done on scalable platforms such as Google Colab or Jupyter Notebook for computational efficiency.

Once trained, the model is integrated into a user-friendly web application built with Flask, allowing users to upload oral images and receive real-time diagnostic results. The system aims to enhance early detection, improve accessibility to diagnostics, and support better oral healthcare outcomes globally.

The literature review highlights multiple studies using InceptionResNetV2 and related deep learning techniques for classifying various oral conditions with high accuracy (often exceeding 90%), including benign and malignant lesions. These studies also explore segmentation, transfer learning, multimodal sensing, and automated classification of histopathological images to improve detection and diagnosis.

The methodology involves data collection and preprocessing, model training with InceptionResNetV2, and deployment via a web interface, with performance evaluated using accuracy, precision, recall, and F1-score metrics.

Conclusion

The proposed system offers a powerful and efficientsolution for early diagnosis and disease identification. By leveraging the advanced capabilities of the InceptionResNetV2 architecture, the system effectively extracts complex features from oral disease images,ensuring high accuracy and reliability in classification. The integration of the trained model into a Flask-based web application provides a user-friendly platform for both medical professionals and individuals to upload imagesand receive real-time diagnostic results [9]. Through rigorous evaluation using metrics such as accuracy, precision,recall, and F1-score, the system demonstrates its robustness and practical applicability. This paperholdssignificantpotential to assist in early detection and timely treatment of oral diseases,ultimatelycontributingtoimprovedoralhealthcare outcomesandreduceddiseaseprogression.Thecombination of advanced deep learning techniques, efficient computational resources, and intuitive user interaction positions this system as a valuable tool in the field of medical diagnostics [10].

References

[1] Rashid, Javed, Bilal Shabbir Qaisar, Muhammad Faheem, Arslan Akram, Riaz ulAmin,andMuhammadHamid.\"Mouth andoraldiseaseclassificationusingInceptionResNetV2 method.\"MultimediaToolsandApplications83,no.11 (2024): 33903-33921. [2] Rajee, M. V., and C. Mythili. \"Dental image segmentation and classification using inception Resnetv2.\" IETE Journal of Research 69, no. 8 (2023): 4972-4988. [3] Kaushik, Pratham, and Saniya Khurana. \"OralGuard: Harnessing Inception-ResNet-v2 for Cutting-Edge OralHealth Diagnostics.\" In 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), pp.882-887. IEEE, 2024. [4] Kumar, K. Vinay, SumanaswiniPalakurthy, Sri Harsha Balijadaddanala, Sharmila Reddy Pappula, and Anil Kumar Lavudya. \"Early Detection and Diagnosis of Oral CancerUsing Deep Neural Network.\" Journal of Computer Allied Intelligence (JCAI, ISSN: 2584-2676) 2, no. 2 (2024): 22-34. [5] Tanriver, Gizem, MervaSolukTekkesin, and OnurErgen. \"Automated detection and classification of oral lesions using deep learning to detect oral potentially malignant disorders.\" Cancers 13, no. 11 (2021): 2766. [6] Soni, Aradhana, Prabira Kumar Sethy, Amit Kumar Dewangan, Aziz Nanthaamornphong, Santi Kumari Behera, and Baishnu Devi. \"Enhancing oral squamous cell carcinoma detection: a novel approach using improved EfficientNet architecture.\" BMC Oral Health 24, no. 1 (2024): 601. [7] Zhang, Hao, Wei Li, and HanzhongZhang.\"[Retracted]An Image Recognition Framework forOralCancerCells.\"Journal of Healthcare Engineering 2021, no. 1 (2021): 2449128. [8] Sudha, G., M. MohammadhaHussani, N.Bagyalakshmi,R. Avanthika, M. Saraswathi, and S. Renuka. \"Digital Diagnosis of Mouth Disease Using Deep Learning Algorithms.\" In 2024 Tenth International Conference on Bio Signals, Images, and Instrumentation (ICBSII), pp. 1-9. IEEE, 2024. [9] Babu, P.Ashok,AnjaniKumarRai,JanjhyamVenkataNaga Ramesh, A. Nithyasri, S. Sangeetha, Pravin R. Kshirsagar, A. Rajendran, A. Rajaram, and S. Dilipkumar. \"An explainable deep learning approach for oral cancer detection.\" Journal of ElectricalEngineering&Technology19,no.3(2024): [10] 1837-1848. [11] Das, Madhusmita, Rasmita Dash, Sambit Kumar Mishra, and Asish Kumar Dalai. \"An Ensemble deep learning modelfor oral squamous cell carcinoma detection using histopathological image analysis.\" IEEE Access (2024).

Copyright

Copyright © 2025 Sunil Zingare, Ayan Lakhani, Prof. Mohit Popat. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.