Abstract

Microscopic examination of tissues to detect oral cancer falls short as traditional microscopes struggle to easily differentiate between cancerous and non-cancerous cells. The identification of cancerous cells through microscopic biopsy images has the potential to alleviate concerns and improve outcomes if precise biological approaches are employed. However, relying solely on physical examinations and microscopic biopsy images for cancer identification increases the likelihood of human error and mistakes. Therefore, in order to obtain accurate results, a new research technique has been developed. In this manuscript, Gazelle Optimized Visual Geometry Group Network with Resnet101 fostered Oral Squamous Cell Carcinoma Detection (OCD-VGGNetCNN-GOA-Resnet101) is proposed. In this method initially, the images are taken from Kaggle repository benchmark dataset and preprocessed to improve image quality.  Then the result is given to the Visual Geometry group Network based CNN (VGGNetCNN) with Resnet101 for classification. Finally, the VGGNetCNN -ResNet 101 classifies image into normal and OSCC. Then the simulation performance of the proposed -VGGNetCNN-GOA-Resnet101 method attains 23.67%, 34.89%, 39.45% and 45.31% higher accuracy while compared with existing methods such as OCD-CNN-Alexnet, OCD-CNN-VGG19 and HI-OCD-CNN-INet respectively.