Oral cancer detection via Vanilla CNN optimized by improved artificial protozoa optimizer

Abstract

In this study, we propose a new method for oral cancer detection using a modified Vanilla Convolutional Neural Network (CNN) architecture with incorporated batch normalization, dropout regularization, and a customized design structure for the convolutional block. An Improved Artificial Protozoa Optimizer (IAPO) metaheuristic algorithm is proposed to optimize the Vanilla CNN and the IAPO improves the original Artificial Protozoa Optimizer through a new search strategy and adaptive parameter tuning mechanism. Due to its effectiveness in search space exploration while avoiding local optimal points, the IAPO algorithm is chosen to optimize the convolutional neural network. In this study, a dataset of 1000 images of patients had published which will be preprocessed with contrast enhancement, noise reduction and data augmentation (like rotation, flipping and cropping) to generate the robust targeted model for oral cancer detection. The experimental results are evaluated against benchmark performance measures (accuracies, precision, recall, F1-score and area under the receiver operating characteristic (ROC) curve (AUC-ROC). We demonstrate through experimental results that the proposed IAPO optimized Vanilla CNN achieves a high accuracy of 92.5% which is superior than the previous state-of-the art models such as ResNet-101 (90.1%) and DenseNet-121 (89.5%). This proves to be a more trustworthy approach to oral cancer detectionbecause of the accuracy of the proposed method in comparison to denoting the supplementary results of the suggested method in contrast to other existing models.