ArticleHealthcare (Basel, Switzerland)2022
Artificial-Intelligence-Based Decision Making for Oral Potentially Malignant Disorder Diagnosis in Internet of Medical Things Environment.
Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed.
- Enhanced Histopathologic Image Analysis for Mouth Cancer Classification Using Morphological Reconstruction and UNet.Journal of imaging informatics in medicine · 2026Article
- Automated classification of oral potentially malignant disorders and oral squamous cell carcinoma using a convolutional neural network framework: a cross-sectional study.Lancet regional health. Americas · 2025Article
- Deep structured learning with vision intelligence for oral carcinoma lesion segmentation and classification using medical imaging.Scientific reports · 2025Article
- Artificial Intelligence in Oral Cancer: A Comprehensive Scoping Review of Diagnostic and Prognostic Applications.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial intelligence and the diagnosis of oral cavity cancer and oral potentially malignant disorders from clinical photographs: a narrative review.Frontiers in oral health · 2025Review
- Intelligent deep learning supports biomedical image detection and classification of oral cancer.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Article
- Quantum leap in the diagnosis of oral potentially malignant disorders - A review of literature.National journal of maxillofacial surgeryReview
- Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Oral cancer is considered one of the most common cancer types in several counties. Earlier-stage identification is essential for better prognosis, treatment, and survival. To enhance precision medicine, Internet of Medical Things (IoMT) and deep learning (DL) models can be developed for automated oral cancer classification to improve detection rate and decrease cancer-specific mortality. This article focuses on the design of an optimal Inception-Deep Convolution Neural Network for Oral Potentially Malignant Disorder Detection (OIDCNN-OPMDD) technique in the IoMT environment. The presented OIDCNN-OPMDD technique mainly concentrates on identifying and classifying oral cancer by using an IoMT device-based data collection process. In this study, the feature extraction and classification process are performed using the IDCNN model, which integrates the Inception module with DCNN. To enhance the classification performance of the IDCNN model, the moth flame optimization (MFO) technique can be employed. The experimental results of the OIDCNN-OPMDD technique are investigated, and the results are inspected under specific measures. The experimental outcome pointed out the enhanced performance of the OIDCNN-OPMDD model over other DL models.
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Registered trials
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.