Enhancing Oral Cancer Diagnosis: IAWMF based Preprocessing in RGB and CT Images

Globally, there are over 350,000 cases of oral cancer, mainly oral squamous cell carcinomas that arise in the mouth and tongue tissues. For metastatic cases, the survival rate drops dramatically from 83% to only 65% if detected early. It is challenging, however, to identify oral lesions and precancerous conditions in their earliest stages when treatment can be most effective. CT scans and intraoral RGB photography are critical imaging methods for screening and diagnosing oral cancers. It is difficult to detect oral cancer accurately with these imaging techniques due to artifacts, noise, and poor lesion visibility. In current CAD methods, preprocessing pipelines for oral cavity images across RGB and CT are not robust or tailored. An automated, adaptive, and multimodal preprocessing pipeline is presented in this study to reduce this gap by facilitating early detection of oral cancer from RGB photographs and CT scans. A region-of-interest cropping technique is used to focus on diagnostically significant areas, specialized noise filters are used to reduce noise while maintaining tissue features, and adaptive histogram equalization is used to normalize contrast dynamically across images and highlight lesions. Oral cavity images in RGB and CT formats were used to evaluate the proposed techniques. As compared to original images, the results demonstrated significant improvements in image quality, noise reduction, lesion conspicuity, and feature visibility. Among the noise filters assessed, the Iterative Adaptive Weighted Median Filter (IAWMF) performed best with PSNR 33, SNR 28.2817, and RMSE 5.7088, which indicated the filter's effectiveness for high fidelity denoising. The median filter was also capable of reducing noise effectively with PSNR of 26.7701, SNR of 22.0518, and RMSE of 11.696. Image quality was the poorest when using Total Generalized Variation. A tailored preprocessing pipeline shows promise in aiding early diagnosis and treatment of oral cancer.