## 3 Application examples of CAD for brain diseases
### Brain glioma  
Accurate noninvasive radiologic diagnosis of brain tumors is desirable for appropriate treatment planning. The diagnosis of brain tumors is usually made subjectively using MR imaging, and its accuracy may be limited by the presence of atypical cases or by a radiologist's insufficient clinical experience. A computerized method that is capable of providing objective information about an image may assist radiologists in the classification of brain tumors. ANNs have been reported to improve the diagnostic performance of radiologists in several fields [94-102]. A few groups applied ANNs including a self-organizing maps (SOMs) to classify intracranial diseases including brain tumors, pituitary adenoma, craniopharyngioma, and [PERSON]'s cleft cyst. [PERSON] _et al._[103] designed a computer-assisted method to perform segmentation of brain tumor on apparent diffusion coefficient (ADC) images and evaluate its grade (malignancy state), using a mixture of unsupervised ANN and hierarchical multiresolution wavelet. As a result, the average sensitivity and the specificity for high or low grade tumor, edema, necrosis, CSF and normal tissue are observed to be 0.86 and 0.93, respectively. [PERSON] _et al._[104] applied an ANN for differential diagnosis among pituitary adenoma, craniopharyngioma, and [PERSON]'s cleft cyst with MR images and retrospectively evaluate the effect of ANN output on radiologists' performance. The ANN showed high performance in differentiation among the three lesions (AUC=0.990). The average AUC for all radiologists for differentiation among the three lesions increased significantly from 0.910 to 0.985 (P = 0.0024) when they used the computer output. We also constructed an ANN for the differential diagnosis of intra-axial cerebral tumors on MR images and evaluated the effect of ANN outputs on radiologists' diagnostic performance [60]. The radiologists collected MR images of 126 patients with intra-axial cerebral tumors (58 high-grade gliomas, 37 low-grade gliomas, 19 metastatic tumors, and 12 malignant lymphomas). We constructed a single 3-layer feed-forward ANN, as shown in Figure 9, with a Levenberg-Marquardt algorithm. The ANN was designed to differentiate among 4 categories of tumors (high-grade gliomas, low-grade gliomas, metastases, and malignant lymphomas) with use of 2 clinical parameters and 13 findings in MR images. Subjective ratings for the 13 findings were provided independently by 2 attending radiologists. Originally, the rating scores were integer, nominal values depending on grade or location, but all inputs used in this study for the ANN were linearly normalized from -0.9 to 0.9. All 126 cases were used for training and testing of the ANN based on a leave-one-out-by-case method.  
In the observer test, MR images were viewed by 9 radiologists, first without and then with ANN outputs. Each radiologist's performance was evaluated through a ROC analysis on a continuous rating scale. The averaged area under the ROC curve for ANN alone was 0.949. The diagnostic performance of the 9 radiologists increased from 0.899 to 0.946 (P <0.001) when they used ANN outputs. The ANN can provide useful outputs as a second opinion to improve radiologists' performance in the differential diagnosis of intra-axial cerebral tumors seen on MR imaging.