Advanced Deep Learning Approaches to Improve Accuracy in the Detection of Oral Cancer

Abstract

Oral cancer exhibits a distressingly high fatality rate and is a widespread and formidable tumor. In India, oral cancer ranks seventh in terms of incidence and causes around 1,30,000 people sufferers annually. Due to this accountable highly impact in mortality rate, this oral cancer posing a serious and significant risk or hazard to public health. The reason for this cancer disease is carcinoma cell, which ranks seventh among all cancer types globally regarding prevalence. It is an essential to boosting patient survival rates and increasing the likelihood of effective therapy is an Early Oral Squamous Cell Carcinoma (OSCC) detection. Traditional diagnosis techniques, such biopsy, which involve taking tiny tissue samples from the diseased region and examining them under a microscope, take time and call for professional analysis. Additionally, proper diagnosis is difficult since OSCC is heterogeneous; hence new methods are required to improve the OSCC image detection. Deep learning algorithms are one such alternative strategy that, by utilizing its sophisticated algorithms and image processing skills, plays a critical part in cancer identification. Hence, the proposed research aims to identify oral cancer in the initial stage, which will enhance treatment results and survival rates using deep learning approaches. newlineIn order to achieve this, the proposed research involving four significant technical contributions to accomplish the efficient oral cancer newlineviii newlinedetection process. The proposal suggests a hybrid approach, termed quotLion ABC,quot for optimizing the segmentation of oral cancer regions. This hybridization aims to leverage the strengths of both LOA and ABC in addressing optimization challenges. The focus is on improving the performance of oral cancer region segmentation, and the motivation for combining these algorithms is to enhance overall effectiveness.