Automated Lion Optimization Algorithm with Deep Transfer Learning Based Oral Cancer Detection and Classification Model

Abstract

Oral cancer (OC) recognition involves leveraging innovative technologies like imaging models and machine learning (ML) techniques to analyze oral cavity anomalies, helping in the initial analysis and enhancing treatment results. These new methods contribute to appropriate intervention and the probable for improved existence rates in individuals in danger of oral cancer. The normal analysis of oral cancer is the microscopic study of specimens detached especially over incisional biopsies of oral mucosa through a clinical spotted suspicious lesion. The use of deep learning (DL) methods is effective in many kinds of cancer; but, a restricted research study has been completed utilizing histopathological OSCC images. Unlike conventional ML, which needs physical feature removal and reflects area expertise, DL can mechanically remove features with an alteration from hand-designed to data-driven features. Despite the customary medical techniques employed in oral classification, automatic models dependent upon a DL framework display promising outcomes. Therefore, this article presents an automated lion optimization algorithm with a deep transfer learning-based oral cancer detection and classification (LOADL-OCDC) methodology. The main intention of the LOADL-OCDC technique is to recognize and categorize the occurrence of oral cancer into distinct classes. The LOADL-OCDC technique follows a multistage process. Initially, the LOADL-OCDC technique performs bilateral filtering-based noise elimination and CLAHE-based contrast improvement. Next, the EfficientNet model can be applied to learn complex and intrinsic feature patterns from the pre-processed images. In the presented LOADL-OCDC technique, the lion optimization algorithm (LOA) can be applied for fine-tuning the hyperparameters of the EfficientNet model. For cancer detection, the LOADL-OCDC technique applies a deep recurrent neural network (DRNN) system. A general experimental study is created to investigate the detection results of the LOADL-OCDC technique. The complete comparison study reported the supremacy of the LOADL-OCDC system in terms of different measures.