Tuberculosis Detection from Computed Tomography with Convolutional Neural Networks

Abstract

Convolutional neural network (CNN), a class of deep neural networks (most commonly used in visual image analysis), has become one of the most influential innovations in the field of computer vision. In our research, we built a system which allows the computer to extract the feature and recognize the image of human lungs and to automatically conclude the health level of the lungs based on database. Here, we built a CNN model to train the datasets. After the training, the system could do certain preliminary analysis already. In addition, we used the fixed coordinate to reduce the noise and combined the Canny algorithm and the Mask algorithm to further improve the accuracy of the system. The final accuracy turned out to be 87.0%, which is convincing. Our system can contribute a lot to the efficiency and accuracy of doctors' analysis of the patients' health level. In the future, we will do more improvement to reduce noise and increase accuracy.