ArticleInternational journal of environmental research and public health2023
Automatic Detection of Oral Squamous Cell Carcinoma from Histopathological Images of Oral Mucosa Using Deep Convolutional Neural Network.
Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Diagnostic performance of convolutional neural network-based AI in detecting oral squamous cell carcinoma: a systematic review and meta-analysis.BMC oral health · 2026Pooled it
- Deep learning in oral cancer- a systematic review.BMC oral health · 2024Pooled it
- Diagnostic performance of artificial intelligence in detecting oral potentially malignant disorders and oral cancer using medical diagnostic imaging: a systematic review and meta-analysis.Frontiers in oral health · 2024Pooled it
- Generalizability of Deep Learning Architectures for Oral Squamous Cell Carcinoma Classification in Histopathology: A Comparative CNN-Vision Transformer Study with External Validation and Label-Quality Analysis.Journal of imaging informatics in medicine · 2026Article
- SSDA_AOA: Stacked Sparse Denoising Autoencoder With Archimedes Optimization Algorithm Based Oral Cancer Detection on Histopathological Images.Oral diseases · 2026Article
- Oral Cancer Diagnosis Using an Optimized InceptionV3 Model Powered by the Aquila Metaheuristic Algorithm.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- Attention-aided hybrid transformer network for oral squamous cell carcinoma classification using histopathology images.Frontiers in artificial intelligence · 2026Article
- A histopathology image-based computer-aided classification study for oral squamous cell carcinoma.Frontiers in oncology · 2026Article
- Domain-generalizable oral cancer classification: bridging multi-center histopathological datasets.Frontiers in medicine · 2026Article
- Performance of deep learning models for the classification and object detection of different oral white lesions using photographic images.Scientific reports · 2025Article
- Enhancing Early Detection of Oral Squamous Cell Carcinoma: A Deep Learning Approach with LRT-Enhanced EfficientNet-B3 for Accurate and Efficient Histopathological Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Updates, Applications and Future Directions of Deep Learning for the Images Processing in the Field of Cranio-Maxillo-Facial Surgery.Bioengineering (Basel, Switzerland) · 2025Review
- Oral squamous cell carcinoma grading classification using deep transformer encoder assisted dilated convolution with global attention.Frontiers in artificial intelligence · 2025Article
- Exploring the efficacy of various CNN architectures in diagnosing oral cancer from squamous cell carcinoma.MethodsX · 2024Article
- Deep transfer learning with improved crayfish optimization algorithm for oral squamous cell carcinoma cancer recognition using histopathological images.Scientific reports · 2024Article
- Enhancing Oral Squamous Cell Carcinoma Detection Using Histopathological Images: A Deep Feature Fusion and Improved Haris Hawks Optimization-Based Framework.Bioengineering (Basel, Switzerland) · 2024Article
- AI illuminates paths in oral cancer: transformative insights, diagnostic precision, and personalized strategies.EXCLI journal · 2024Review
- A Deep Learning Framework with an Intermediate Layer Using the Swarm Intelligence Optimizer for Diagnosing Oral Squamous Cell Carcinoma.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence for Image Analysis in Oral Squamous Cell Carcinoma: A Review.Diagnostics (Basel, Switzerland) · 2023Review
- OralNet: Fused Optimal Deep Features Framework for Oral Squamous Cell Carcinoma Detection.Biomolecules · 2023Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Worldwide, oral cancer is the sixth most common type of cancer. India is in 2nd position, with the highest number of oral cancer patients. To the population of oral cancer patients, India contributes to almost one-third of the total count. Among several types of oral cancer, the most common and dominant one is oral squamous cell carcinoma (OSCC). The major reason for oral cancer is tobacco consumption, excessive alcohol consumption, unhygienic mouth condition, betel quid eating, viral infection (namely human papillomavirus), etc. The early detection of oral cancer type OSCC, in its preliminary stage, gives more chances for better treatment and proper therapy. In this paper, author proposes a convolutional neural network model, for the automatic and early detection of OSCC, and for experimental purposes, histopathological oral cancer images are considered. The proposed model is compared and analyzed with state-of-the-art deep learning models like VGG16, VGG19, Alexnet, ResNet50, ResNet101, Mobile Net and Inception Net. The proposed model achieved a cross-validation accuracy of 97.82%, which indicates the suitability of the proposed approach for the automatic classification of oral cancer data.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.