Neural network approach for computer-assisted interpretation of ultrasound images of the gallbladder

Multi-formatted films of 90 ultrasound examinations of the gallbladder (stones 56 cases, sludge 20 cases, hydrops five cases, normal gallbladder nine cases) have been digitalized and stored in a personal computer. Image data of each examination was processed to extract a 19-dimensional vector that represents the essential diagnostic information of each examination. This vector was evaluated by three different classification algorithms: (1) classical nearest neighbor principle, (2) classical linear discriminant analysis, (3) multilayered backpropagation neural network. The correct classification rate was 64% (58/90) for the nearest neighbor principle, 97% (87/90) for the linear discriminant analysis, and 99% (89/90) for the backpropagation neural network. We conclude that, (1) automated classification of ultrasound images is possible for limited diagnostic problems, (2) a neural network approach can be used successfully for that goal, and (3) the efficiency of the more flexible neural network approach is comparable to large-scale classical methods.