ArticleComputational intelligence and neuroscience2022
Diagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approach.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled 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
- Artificial Intelligence in Oral and Maxillofacial Surgery-A Narrative Review.Journal of clinical medicine · 2026Review
- A self-supervised pre-training method for lesion segmentation in oral potentially malignant disorders.BMC oral health · 2026Article
- Explainable CNN with spatial attention for oral cancer patch selection using a custom scoring metric.Biomedizinische Technik. Biomedical engineering · 2026Article
- Artificial intelligence for diagnosis and triage in oral cancer: a clinician‑centered narrative review.International journal of clinical oncology · 2026Review
- Domain-generalizable oral cancer classification: bridging multi-center histopathological datasets.Frontiers in medicine · 2026Article
- Texture-based image feature analysis for the classification of oral squamous cell carcinoma using machine learning approach.BMC oral health · 2025Article
- Artificial Intelligence Tools in Dentistry: A Systematic Review on Their Application and Outcomes.Cureus · 2025Review
- Artificial Intelligence in Oral Cancer: A Comprehensive Scoping Review of Diagnostic and Prognostic Applications.Diagnostics (Basel, Switzerland) · 2025Review
- Oral squamous cell carcinoma grading classification using deep transformer encoder assisted dilated convolution with global attention.Frontiers in artificial intelligence · 2025Article
- Improving laryngeal cancer detection using chaotic metaheuristics integration with squeeze-and-excitation resnet model.Health information science and systems · 2024Article
- Research and Application of Deep Learning Models with Multi-Scale Feature Fusion for Lesion Segmentation in Oral Mucosal Diseases.Bioengineering (Basel, Switzerland) · 2024Article
- Deep transfer learning with improved crayfish optimization algorithm for oral squamous cell carcinoma cancer recognition using histopathological images.Scientific reports · 2024Article
- Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Development of a hybrid LSTM with chimp optimization algorithm for the pressure ventilator prediction.Scientific reports · 2023Article
- Article
- Application and Performance of Artificial Intelligence (AI) in Oral Cancer Diagnosis and Prediction Using Histopathological Images: A Systematic Review.Biomedicines · 2023Review
- A Current Review of Machine Learning and Deep Learning Models in Oral Cancer Diagnosis: Recent Technologies, Open Challenges, and Future Research Directions.Diagnostics (Basel, Switzerland) · 2023Review
- Oral squamous cell carcinoma detection using EfficientNet on histopathological images.Frontiers in medicine · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Overall prediction of oral cavity squamous cell carcinoma (OCSCC) remains inadequate, as more than half of patients with oral cavity cancer are detected at later stages. It is generally accepted that the differential diagnosis of OCSCC is usually difficult and requires expertise and experience. Diagnosis from biopsy tissue is a complex process, and it is slow, costly, and prone to human error. To overcome these problems, a computer-aided diagnosis (CAD) approach was proposed in this work. A dataset comprising two categories, normal epithelium of the oral cavity (NEOR) and squamous cell carcinoma of the oral cavity (OSCC), was used. Feature extraction was performed from this dataset using four deep learning (DL) models (VGG16, AlexNet, ResNet50, and Inception V3) to realize artificial intelligence of medial things (AIoMT). Binary Particle Swarm Optimization (BPSO) was used to select the best features. The effects of Reinhard stain normalization on performance were also investigated. After the best features were extracted and selected, they were classified using the XGBoost. The best classification accuracy of 96.3% was obtained when using Inception V3 with BPSO. This approach significantly contributes to improving the diagnostic efficiency of OCSCC patients using histopathological images while reducing diagnostic costs.
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