Performance Evaluation of Oral Cancer Detection and Classification using Deep Learning Approach

Oral Cancer disease is the major reason behind increased no. of death rates. Detecting and preventing Oral cancer at the earliest, can be beneficial in minimizing the death rate and increasing the life span. The research proposes a technique concerning Oral cancer detection and prevention by adopting DL (Deep Learning) approaches. Data analysis relies upon history of addiction, clinical symptoms, co-morbid condition and survivability factor related to the cancer patients. This research work aims to segment and classify the oral cancer and measure the performance metrics using these intelligent computing techniques. The oral cancer detection and Classification Using Deep Learning Approach contribution was based on Fragment Jaya Whale Optimizer with Deep Convolutional Neural Network (FJWO-DCNN) for achieving a good recognition rate using the optimal theoretical features of oral cancer images. Then the extracted theoretical features and the original image were given as the input to different level classification strategy which were performed using Deep Convolution Neural Network (DCNN) classifier, which was trained by the proposed Fragment Jaya Whale Optimization (FJWO) algorithm. Finally, employing this approach, the results were obtained with a higher performance value. This proposed work achieves a higher performance percentage in terms of accuracy.