ArticleDiagnostics (Basel, Switzerland)2022
Early Diagnosis of Oral Squamous Cell Carcinoma Based on Histopathological Images Using Deep and Hybrid Learning Approaches.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 3 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Diagnostic Accuracy of Artificial Intelligence Compared to Biopsy in Detecting Early Oral Squamous Cell Carcinoma: A Systematic Review and Meta Analysis.Asian Pacific journal of cancer prevention : APJCP · 2024Pooled 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
- Agricultural sustainability monitoring in arid regions using hybrid deep learning and Landsat 8 imagery in Najran City, Saudi Arabia.Scientific reports · 2026Article
- Attention-aided hybrid transformer network for oral squamous cell carcinoma classification using histopathology images.Frontiers in artificial intelligence · 2026Article
- Artificial intelligence and its application in early oral cancer screening: a systematic review.Frontiers in oncology · 2026Review
- Non-Invasive Techniques for Early Detection of Oral Squamous Cell Carcinoma: A Narrative Review.Cancer management and research · 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
- "Enhancing early detection of oral cancer: a comparative study of artificial intelligence models and clinical specialist in lesion classification".BMC cancer · 2025Article
- Hybrid deep learning and optimization-based land use and land cover classification for advancing sustainable agriculture in Najran city, Saudi Arabia.Scientific reports · 2025Article
- Review
- Diagnostic value of circulating lncRNAs HOTAIR, ANRIL, and MEG3 in oral squamous cell carcinoma and their correlation with clinicopathological features.The journal of liquid biopsy · 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
- Artificial Intelligence Tools in Dentistry: A Systematic Review on Their Application and Outcomes.Cureus · 2025Review
- Artificial intelligence based classification and prediction of medical imaging using a novel framework of inverted and self-attention deep neural network architecture.Scientific reports · 2025Article
- Artificial Intelligence in Oral Cancer: A Comprehensive Scoping Review of Diagnostic and Prognostic Applications.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial intelligence-driven clinical decision support systems for early detection and precision therapy in oral cancer: a mini review.Frontiers in oral health · 2025Review
- Fusion feature-based hybrid methods for diagnosing oral squamous cell carcinoma in histopathological images.Frontiers in oncology · 2025Article
- Exploring the efficacy of various CNN architectures in diagnosing oral cancer from squamous cell carcinoma.MethodsX · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Oral squamous cell carcinoma (OSCC) is one of the most common head and neck cancer types, which is ranked the seventh most common cancer. As OSCC is a histological tumor, histopathological images are the gold diagnosis standard. However, such diagnosis takes a long time and high-efficiency human experience due to tumor heterogeneity. Thus, artificial intelligence techniques help doctors and experts to make an accurate diagnosis. This study aimed to achieve satisfactory results for the early diagnosis of OSCC by applying hybrid techniques based on fused features. The first proposed method is based on a hybrid method of CNN models (AlexNet and ResNet-18) and the support vector machine (SVM) algorithm. This method achieved superior results in diagnosing the OSCC data set. The second proposed method is based on the hybrid features extracted by CNN models (AlexNet and ResNet-18) combined with the color, texture, and shape features extracted using the fuzzy color histogram (FCH), discrete wavelet transform (DWT), local binary pattern (LBP), and gray-level co-occurrence matrix (GLCM) algorithms. Because of the high dimensionality of the data set features, the principal component analysis (PCA) algorithm was applied to reduce the dimensionality and send it to the artificial neural network (ANN) algorithm to diagnose it with promising accuracy. All the proposed systems achieved superior results in histological image diagnosis of OSCC, the ANN network based on the hybrid features using AlexNet, DWT, LBP, FCH, and GLCM achieved an accuracy of 99.1%, specificity of 99.61%, sensitivity of 99.5%, precision of 99.71%, and AUC of 99.52%.
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