Advanced deep learning algorithms in oral cancer detection: Techniques and applications

作     者：Wankhade, Dipali Dhawale, Chitra Meshram, Mrunal 

作者机构：Datta Meghe Inst Higher Educ & Res Wardha Nagpur Nagpur India Datta Meghe Inst Higher Educ & Res Deemed Univ Fac Sci & Technol Wardha India Sharad Pawar Dent Collage Dept Oral Med & Radiol Wardha India 

出 版 物：《JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH PART C-TOXICOLOGY AND CARCINOGENESIS》 (J. Environ. Sci. Health Toxicol. Carcinogen.)

年 卷 期：2025年第43卷第2期

页      面：133-158页

核心收录：

基　　金：The author(s) reported there is no funding associated with the work featured in this article 

主　　题：Oral cancer detection artificial intelligence deep learning convolutional neural networks machine learning early diagnosis medical imaging image processing oral squamous cell carcinoma predictive modeling 

摘      要：As the 16th most common cancer globally, oral cancer yearly accounts for some 355,000 new cases. This study underlines that an early diagnosis can improve the prognosis and cut down on mortality. It discloses a multifaceted approach to the detection of oral cancer, including clinical examination, biopsies, imaging techniques, and the incorporation of artificial intelligence and deep learning methods. This study is distinctive in that it provides a thorough analysis of the most recent AI-based methods for detecting oral cancer, including deep learning models and machine learning algorithms that use convolutional neural networks. By improving the precision and effectiveness of cancer cell detection, these models eventually make early diagnosis and therapy possible. This study also discusses the importance of techniques in image pre-processing and segmentation in improving image quality and feature extraction, an essential component of accurate diagnosis. These techniques have shown promising results, with classification accuracies reaching up to 97.66% in some models. Integrating the conventional methods with the cutting-edge AI technologies, this study seeks to advance early diagnosis of oral cancer, thus enhancing patient outcomes and cutting down on the burden this disease is imposing on healthcare systems.