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Advanced deep learning algorithms in oral cancer detection: Techniques and applications
Dipali Wankhade1, Chitra Dhawale2, Mrunal Meshram3
1Research Scholar, Datta Meghe Institute of Higher Education and Research Wardha, Nagpur, India.
Summary
Early oral cancer detection using artificial intelligence (AI) and deep learning improves prognosis. This study analyzes AI methods, achieving up to 97.66% accuracy for earlier diagnosis and better patient outcomes.
Area of Science:
- Oncology
- Medical Imaging
- Artificial Intelligence
Background:
- Oral cancer is the 16th most common globally, with 355,000 new cases annually.
- Early diagnosis significantly improves patient prognosis and reduces mortality rates.
- Conventional detection methods include clinical examination, biopsies, and imaging.
Purpose of the Study:
- To analyze recent artificial intelligence (AI) and deep learning methods for oral cancer detection.
- To evaluate the integration of AI with conventional diagnostic techniques.
- To enhance the precision and effectiveness of early oral cancer diagnosis.
Main Methods:
- Review of AI-based methods, including deep learning models and convolutional neural networks.
- Analysis of image pre-processing and segmentation techniques for improved image quality and feature extraction.
- Evaluation of classification accuracies of various AI models.
Main Results:
- AI and deep learning models show promise in improving oral cancer detection.
- Image pre-processing and segmentation are crucial for accurate diagnosis.
- Some AI models achieved classification accuracies up to 97.66%.
Conclusions:
- Integrating AI with conventional methods advances early oral cancer diagnosis.
- Improved diagnostic accuracy leads to enhanced patient outcomes.
- AI can help reduce the healthcare burden of oral cancer.
