ArticleSensors (Basel, Switzerland)2022
Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed, 135 citations in OpenAlex.
- Oral squamous cell carcinoma diagnosis: an interpretable deep learning approach using multi-scale pathological images.BMC oral health · 2026Article
- SSDA_AOA: Stacked Sparse Denoising Autoencoder With Archimedes Optimization Algorithm Based Oral Cancer Detection on Histopathological Images.Oral diseases · 2026Article
- Efficient deep learning models for oral squamous cell carcinoma classification in histopathological images.Scientific reports · 2026Article
- Advanced analysis and detection of cervical cancer using NASNetLarge.Scientific reports · 2026Article
- Deep visual detection system for oral squamous cell carcinoma.Scientific reports · 2026Article
- Domain-generalizable oral cancer classification: bridging multi-center histopathological datasets.Frontiers in medicine · 2026Article
- Attention-aided hybrid transformer network for oral squamous cell carcinoma classification using histopathology images.Frontiers in artificial intelligence · 2026Article
- Using transfer learning approaches to predict RNA-Seq gene expression data for cancer classification.Frontiers in artificial intelligence · 2026Article
- Texture-based image feature analysis for the classification of oral squamous cell carcinoma using machine learning approach.BMC oral health · 2025Article
- Machine Learning Based Multi-Class Classification and Grading of Squamous Cell Carcinoma in Optical Microscopy.Microscopy research and technique · 2025Article
- The interactions of Fusobacterium nucleatum and Porphyromonas gingivalis with microRNAs: contributions to oral squamous cell carcinoma.Molecular biology reports · 2025Article
- Explainable AI for Oral Cancer Diagnosis: Multiclass Classification of Histopathology Images and Grad-CAM Visualization.Biology · 2025Article
- Transfer learning for accurate brain tumor classification in MRI: a step forward in medical diagnostics.Discover oncology · 2025Article
- 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
- Explainable label guided lightweight network with axial transformer encoder for early detection of oral cancer.Scientific reports · 2025Article
- 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
- OralEpitheliumDB: A Dataset for Oral Epithelial Dysplasia Image Segmentation and Classification.Journal of imaging informatics in medicine · 2024Article
- Evaluation of transfer ensemble learning-based convolutional neural network models for the identification of chronic gingivitis from oral photographs.BMC oral health · 2024Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image. In this study, the proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model. Simulation results have shown that the proposed model achieved higher classification accuracy 97.66% and 90.06% of training and testing, respectively.
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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.