ArticleData in brief2020
Histopathological imaging database for oral cancer analysis.
Article in Data in brief, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Deep learning-based automated detection of oral squamous cell carcinoma in histopathological images: a comparative study of five CNN architectures.Odontology · 2026Article
- A narrative review of artificial intelligence in dental imaging: from dataset design to clinical translation.Oral radiology · 2026Review
- Article
- Oral squamous cell carcinoma diagnosis: an interpretable deep learning approach using multi-scale pathological images.BMC oral health · 2026Article
- Automatic Oral Cancer Detection Using Improved Honey Badger Algorithm-Based Feature Selection.Diagnostics (Basel, Switzerland) · 2026Article
- Explainable CNN with spatial attention for oral cancer patch selection using a custom scoring metric.Biomedizinische Technik. Biomedical engineering · 2026Article
- SSDA_AOA: Stacked Sparse Denoising Autoencoder With Archimedes Optimization Algorithm Based Oral Cancer Detection on Histopathological Images.Oral diseases · 2026Article
- A High Magnifications Histopathology Image Dataset for Oral Squamous Cell Carcinoma Diagnosis and Prognosis.Scientific data · 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
- Browser-Based Multi-Cancer Classification Framework Using Depthwise Separable Convolutions for Precision Diagnostics.Diagnostics (Basel, Switzerland) · 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
- Quantitative assessment of biomechanical changes in oral lesions at different cancerous stages using optical coherence elastography.Quantitative imaging in medicine and surgery · 2025Article
- Fusion feature-based hybrid methods for diagnosing oral squamous cell carcinoma in histopathological images.Frontiers in oncology · 2025Article
- Fine-tuned deep transfer learning: an effective strategy for the accurate chronic kidney disease classification.PeerJ. Computer science · 2025Article
- Exploring the efficacy of various CNN architectures in diagnosing oral cancer from squamous cell carcinoma.MethodsX · 2024Article
- Oral Mucosa Capillaroscopy: A Narrative Review.Cancers · 2024Review
- Deep transfer learning with improved crayfish optimization algorithm for oral squamous cell carcinoma cancer recognition using histopathological images.Scientific reports · 2024Article
- High-resolution AI image dataset for diagnosing oral submucous fibrosis and squamous cell carcinoma.Scientific data · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The repository is composed of 1224 images divided into two sets of images with two different resolutions. First set consists of 89 histopathological images with the normal epithelium of the oral cavity and 439 images of Oral Squamous Cell Carcinoma (OSCC) in 100x magnification. The second set consists of 201 images with the normal epithelium of the oral cavity and 495 histopathological images of OSCC in 400x magnification. The images were captured using a Leica ICC50 HD microscope from Hematoxyline and Eosin (H&E) stained tissue slides collected, prepared and catalogued by medical experts from 230 patients. A subset of 269 images from the second data set was used to detect OSCC based on textural features [1]. Histopathology plays a very important role in diagnosing a disease. It is the investigation of biological tissues to detect the presence of diseased cells in microscopic detail. It usually involves a biopsy. Till date biopsy is the gold-standard test to diagnose cancer. The biopsy slides are examined based on various cytological criteria under a microscope. Therefore, there is a high possibility of not retaining uniformity and ensuring reproducibility in outcomes [2, 3]. Computational diagnostic tools, on the other hand, facilitate objective judgments by making the use of the quantitative measure. This dataset can be utilized in establishing automated diagnostic tool using Artificial Intelligence approaches.
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