Even though it has proven challenging to achieve, computerised categorization of cell pictures into fit and aggressive cells would be a crucial tool in diagnostic procedures. It has been demonstrated that texture detection and processing are extremely efficient for a variety of picture categorization algorithms. Recent articles have made use of Dense Networks (DENSENETs), a texture-based method that has shown to have a lot of potential. Some of these variations employ convolutional neural networks using DENSENETs (CNNs). This work modifies modern texture analysis CNN structures, three, and two of which are based on DENSENETs, to recognize pictures from a collection including both healthy and oral cancer cells. Results from Wieslander and Forslid's use of ResNet and VGG architectures, which weren't designed with texture detection in mind, to use as a benchmark. Our research shows that DENSENET-Embedded CNNs outperform conventional CNNs for this job designs. The performance model by Juefei-Xu ET altop exceeded the best reference model by 0.5 percent in accuracy and 9 percent in F1-score. It had an accuracy of 81.03 percent and an F1-score of 84.85 percent.
