Improving Diagnostic Accuracy: Hybrid DeepLearning Model for Oral cancer Identification in Neuroimaging

Oral cancer is a significant health menace as it accounts for many deaths. This is due to the fact that the majority of cases are diagnosed in advanced stages. The timely intervention in case of any diagnosis of oral cancer or any potential pre-cancerous lesions is likely to have better outcomes and reduce the costs incurred in treatment. This review scrutinizes the presently available diagnostic methods which include clinical examination, imaging, molecular approach and other new technologies that are on the way. The article further explores limitations of these approaches and discusses future prospects in diagnosis, especially the part that addresses the need to improve early diagnosis methods. Overall, oral cancer continues to be a significant burden on the health of populations and to improve peoples' outcomes early diagnosis is of utmost importance. It cannot be overemphasized the changes brought about in medical imaging by the advent of deep learning techniques which offer promising solutions for oral cancer detection and classification. One does not need to wait to diagnose this condition and so early identification is imperative. Nevertheless, fundamentals of traditional early diagnosis depend entirely on human skills hence are limited. Various research studies have explored deep learning architecture for different tasks in the diagnosis and classification of oral cancer such as lesion detection, segmentation, and characterization.