Background: Applied research on artificial intelligence, mainly in deep learning, is widely performed. If medical images can be evaluated using artificial intelligence, this could substantially improve examination efficiency.
Objective: We investigated an evaluation system for medical images with different noise characteristics using a deep convolutional neural network.
Methods: Simulated computed tomography images are the targets of the system. We used an AlexNet trained with natural images for the deep convolutional neural network and a support vector machine for classification. Synthetic computed tomography images with circular and rectangular signal bodies at different levels of contrast and added Gaussian noise were used for training and testing.
Results: Two transfer learning methods were tested: classification by a re-trained support vector machine using the AlexNet features, and a method that fine-tuned the deep convolutional neural network. Using the first method, all the test image noise levels could be classified correctly. The fine-tuning method achieved an accuracy rate of 92.6%.
Conclusions: An image quality evaluation method using artificial intelligence will be useful for clinical images and different image quality indices in the future.
Keywords: Classification; computed tomography (CT); deep convolutional neural network (DCNN); noise; phantom.