Identification and Classification of Oral Cancer Using Convolution Neural Network (International Journal of Innovative Research in Computer Science & Technology, 2022)
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.
Keywords: CNN; VGG; Oral Cancer