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Computer-aided Diagnosis Applied to US of Solid Breast Nodules by Using Neural Networks¹

¹ From the Department of General Surgery, China Medical College and Hospital, 2 Yer-Der Rd, Taichung, Taiwan, Republic of China (D.R.C.), and the Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan, Republic of China (R.F.C., Y.L.H.). Received May 16, 1998; revision requested July 30; final revision received April 22, 1999; accepted June 8. Address reprint requests to D.R.C. (e-mail: dlchen88@ms13.hinet.net).

View larger version (48K): [in a new window]   Figure 1. A, Transverse digital US image depicts a malignant tumor, with a resolution of 736 x 556 pixels. Note that there are 58 x 58 = 3,364 pixels in a 1 x 1-cm rectangle. B, The region-of-interest rectangle is approximately 3.1 x 1.5 cm and is captured with a resolution of 179 x 88 pixels. ROI = region of interest.

View larger version (88K): [in a new window]   Figure 2a. Typical transverse US region-of-interest subimages in the breast depict (a) a benign lesion (arrows) and (b) a malignant lesion (arrows).

View larger version (97K): [in a new window]   Figure 2b. Typical transverse US region-of-interest subimages in the breast depict (a) a benign lesion (arrows) and (b) a malignant lesion (arrows).

View larger version (18K): [in a new window]   Figure 3. Structural graph depicts the multilayer feed-forward neural network used in this study. Numbers indicate the number of nodes.

View larger version (22K): [in a new window]   Figure 4. Diagram depicts the structure of the neural network model for tissue classification. ROI = region of interest, 2D = two-dimensional.

View larger version (55K): [in a new window]   Figure 5. Illustration of the program for the neural network diagnostic model. The weights of the neural network are loaded, and then the region-of-interest subimage file is downloaded. If the output of neural network is larger than a predefined threshold of 20, the result will be "probably malignant." If the output is less than the threshold, the result will be "probably benign." Note that the output of the neural network is multiplied by 100. NN = neural network, ROI = region of interest.

View larger version (14K): [in a new window]   Figure 6. Plot depicts the mean differences between the autocorrelation coefficients for the malignant and benign tumors. The mean differences are increasing for larger m and n.

View larger version (7K): [in a new window]   Figure 7. Plot depicts the ROC curve for the neural network in the classification of malignant and benign tumors. The AZ value of the ROC curve is 0.9560 ± 0.0183. FPF = false-positive fraction, TPF = true-positive fraction.