Detection of Oral Cancer Using Probabilistic Neural Network
The number of people being diagnosed positive with oral cancer and its death rate is rapidly increasing every day. This paper aims at enhancing the efficiency of oral cancer diagnosis. The proposed method will detect and classify the affected cancer cell in the oral region using digital Image processing techniques. Initially, the data set images are pre-processed and its contrast is enhanced thus making the images a lot more effective for the other processes involved in detecting oral cancer. Discrete Wavelet Transform (DWT) and Gray Level Co-occurrence Matrix (GLCM) are used for extraction of higher order gradient features for the particular Region Of Interest(ROI) which is determined using the Extraction Maximization Image segmentation process. Maximum Gaussian Mixture model(MGMM) is choose as the pixel classifier. Probabilistic Neural Network (PNN) is used as a classifier here to predict and classify whether the input image is diagnosed with Oral cancer or not.