Oral cancer ranks among the most prevalent cancers globally, with a particularly high mortality rate in regions lacking adequate healthcare access. Early diagnosis is crucial for reducing mortality; however, challenges persist due to limited oral health programs, inadequate infrastructure, and a shortage of healthcare practitioners. Conventional deep learning models, while promising, often rely on point estimates, leading to overconfidence and reduced reliability. Critically, these models require large datasets to mitigate overfitting and ensure generalizability, an unrealistic demand in settings with limited training data. To address these issues, we propose a hybrid model that combines a convolutional neural network (CNN) with Bayesian deep learning for oral cancer classification using small training sets. This approach employs variational inference to enhance reliability through uncertainty quantification. The model was trained on photographic color images captured by smartphones and evaluated on three distinct test datasets. The proposed method achieved 94% accuracy on a test dataset with a distribution similar to that of the training data, comparable to traditional CNN performance. Notably, for real-world photographic image data, despite limitations and variations differing from the training dataset, the proposed model demonstrated superior generalizability, achieving 88% accuracy on diverse datasets compared to 72.94% for traditional CNNs, even with a smaller dataset. Confidence analysis revealed that the model exhibits low uncertainty (high confidence) for correctly classified samples and high uncertainty (low confidence) for misclassified samples. These results underscore the effectiveness of Bayesian inference in data-scarce environments in enhancing early oral cancer diagnosis by improving model reliability and generalizability.

Oral cancer presents a formidable challenge in oncology, necessitating early diagnosis and accurate prognosis to enhance patient survival rates. Recent advancements in machine learning and data mining have revolutionized traditional diagnostic methodologies, providing sophisticated and automated tools for differentiating between benign and malignant oral lesions. This study presents a comprehensive review of cutting-edge data mining methodologies, including Neural Networks, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and ensemble learning techniques, specifically applied to the diagnosis and prognosis of oral cancer. Through a rigorous comparative analysis, our findings reveal that Neural Networks surpass other models, achieving an impressive classification accuracy of 93,6 % in predicting oral cancer. Furthermore, we underscore the potential benefits of integrating feature selection and dimensionality reduction techniques to enhance model performance. These insights underscore the significant promise of advanced data mining techniques in bolstering early detection, optimizing treatment strategies, and ultimately improving patient outcomes in the realm of oral oncology.

Oral cancer constitutes a significant global health concern, resulting in 277,484 fatalities in 2023, with the highest prevalence observed in low- and middle-income nations. Facilitating automation in the detection of possibly malignant and malignant lesions in the oral cavity could result in cost-effective and early disease diagnosis. Establishing an extensive repository of meticulously annotated oral lesions is essential. In this research photos are being collected from global clinical experts, who have been equipped with an annotation tool to generate comprehensive labelling. This research presents a novel approach for integrating bounding box annotations from various doctors. Additionally, Deep Belief Network combined with CAPSNET is employed to develop automated systems that extracted intricate patterns to address this challenging problem. This study evaluated two deep learning-based computer vision methodologies for the automated detection and classification of oral lesions to facilitate the early detection of oral cancer: image classification utilizing CAPSNET. Image classification attained an F1 score of 94.23% for detecting photos with lesions 93.46% for identifying images necessitating referral. Object detection attained an F1 score of 89.34% for identifying lesions for referral. Subsequent performances are documented about classification based on the sort of referral decision. Our preliminary findings indicate that deep learning possesses the capability to address this complex problem.

As numerous biomedical research articles are published regularly, adding knowledge to the accumulated literature on different diseases, such as cancer, neurodegenerative diseases, and hereditary diseases. One of the leading causes of global mortality disease is cancer due to various reasons such as lifestyle habits, radiation exposure, viral infections, and tobacco consumption [1] [2]. These reasons ultimately make some genetic change in a cell of tissue which causes it to become cancerous. Due to the top priority given to cancer research compared to other human diseases, enormous articles were published [3] [4] in a short period [5]. It can serve as a relevant source for cancer knowledge discovery in different fields of diagnostics, application of drugs, genetic association, prevention, and treatment. An automate downloading of articles and extraction of related entities will advance the progression of the research faster. Natural Language Processing (NLP) helps in communicating computers with humans in their language and converts the unstructured data into structured data to improve the accuracy of text mining. NLP function guides to understanding the human query language to discover knowledge from literature without much manual effort [6]. Named Entity Recognition (NER) and text classification is used mainly for text mining [7].

New Delhi, May 28 (PTI) An artificial intelligence-driven smartphone app has been launched by AIIMS-Delhi along with Nurithm labs, a start-up, to address the access and accuracy problems in clinical diagnosis of dermatological diseases, including skin and oral cancers. DermaAId, the skin disease diagnostic solution, uses a machine-learning AI-driven algorithm encapsulated in a mobile app and transforms a basic smartphone with a 1 MP camera into a potent tool in skincare, Dr Somesh Gupta, a Professor in the Department of Dermatology and Venereology at AIIMS, told PTI. For general practitioners, it is a clinical decision support tool to augment their capability and understanding of skin conditions. This is particularly relevant since studies have revealed that diagnostic accuracy among general practitioners vis-à-vis dermatologists is 40 to 50 per cent, Dr Gupta pointed out. "The technology behind the app is deceptively simple. A doctor takes a photo of lesions on a patient's body and uploads them to the cloud server. Within 15-30 seconds, the app provides possible disease conditions based on machine analysis of images," he explained.

Can artificial intelligence help better predict mouth cancer risk?  & nbspPhoto Credit: iStock Images Artificial intelligence (AI) may help doctors better predict the risk of patients developing oral cancer by ensuring accuracy, consistency and objectivity, according to researchers from the University of Sheffield in the UK. The researchers are examining the use of AI and machine learning -- the study of computer algorithms that improve automatically through experience -- to assist pathologists and improve the early detection of oral cancer. The rate of people being diagnosed with oral cancers including mouth, tongue, tonsil and oropharyngeal cancer, has increased by almost 60 per cent in the last 10 years, the researchers said in a statement. Evidence suggests tobacco and alcohol consumption, viruses, old age as well as not eating enough fruit and vegetables can increase the risk of developing the disease, they said. Oral cancer is often detected late which means that the patient survival rates are poor.

Artificial intelligence (AI) may help doctors better predict the risk of patients developing oral cancer by ensuring accuracy, consistency and objectivity, according to researchers from the University of Sheffield in the U.K. The researchers are examining the use of AI and machine learning -- the study of computer algorithms that improve automatically through experience -- to assist pathologists and improve the early detection of oral cancer. The rate of people being diagnosed with oral cancers including mouth, tongue, tonsil and oropharyngeal cancer, has increased by almost 60% in the last 10 years, the researchers said in a statement. Evidence suggests tobacco and alcohol consumption, viruses, old age as well as not eating enough fruit and vegetables can increase the risk of developing the disease, they said. Oral cancer is often detected late which means that the patient survival rates are poor. Currently, doctors must predict the likelihood of pre-cancerous changes, known as oral epithelial dysplasia (OED), developing into cancer by assessing a patient's biopsy on 15 different criteria to establish a score.

The diagnosis of oral cancer could be'revolutionised' by using artificial intelligence to predict whether someone is likely to develop the disease, experts have said. Experts led from the Universities of Sheffield and Warwick have teamed up to investigate how machine learning could be applied to aid doctors in early detection. Diagnoses of oral cancers -- including those of the mouth, tongue and tonsils -- have increased by almost 60 per cent over the last decade, team noted. The risk of such cancers is heightened by such factors as alcohol consumption, increasing age, insufficient fruit and vegetables, tobacco and viral infection. Doctors evaluate the likelihood of pre-cancerous changes in the lining of the mouth -- so-called oral epithelial dysplasia -- developing into cancer using 15 criteria.