A method of detecting tonsillitis images based on medical knowledge and neural network
Tonsillitis is a disease occurring mostly in child and adults as this disease may take to the other effects. Nowadays, a detection of tonsil grand exploits medical doctor’s diagnosis to check on oral cavity. Therefore, this paper presents a diagnosis of tonsillitis using image processing and neural network (NN). There are three steps described as follows. The first step is localization of tonsil grand (TG) using Ellipses Hough Transform. The second step is feature extraction using three important factors which can be indicated in swelling by images of TG in terms of a) a dimensional ratio of TG, b) an average intensity of TG and c) a purulent surface of TG (yes/no) using power spectrum of two dimensional Fast Fourier Transform (2D FFT). The final step is verification using NN. The three factors are inputted into NN, and TG samples of 50 images are used for training into the NN. They are divided by tonsillitis patience 25 images and usual TG of 25 images. This experiment uses 100 images of TG for testing NN. Therefore, the overall accuracy is at approximately 90% in terms of comparing with the results from the medical doctor.