Classification of mammographic masses using generalized dynamic fuzzy neural networks

Wei Keat Lim; Meng Joo Er

doi:10.1117/12.481104

15 May 2003 Classification of mammographic masses using generalized dynamic fuzzy neural networks

Wei Keat Lim, Meng Joo Er

Proceedings Volume 5032, Medical Imaging 2003: Image Processing; (2003) https://doi.org/10.1117/12.481104
Event: Medical Imaging 2003, 2003, San Diego, California, United States

Abstract

In this paper, computer-aided classification of mammographic masses using generalized dynamic fuzzy neural networks (GDFNN) is presented. The texture parameters, derived from first-order gradient distribution and gray-level co-occurrence matrices (GCMs), were computed from the regions of interest (ROIs). A total of 77 images containing 38 benign cases and 39 malignant cases from the Digital Database for Screening Mammography (DDSM) were analyzed. A fast approach of automatically generating fuzzy rules from training samples was implemented to classify tumors. The novelty of this work is that it alleviates the problem of the conventional computer-aided diagnosis (CAD) system that requires a designer to examine all the input-output relationships of a training database in order to obtain the most appropriate structure for the classifier. In this approach, not only the connection weights can be adjusted, but also the structure can be self-adaptive during the learning process. With the classifier automatically generated by the GDFNN learning algorithm, the area under the receiver-operating characteristic (ROC) curve, Az, reached 0.9289, which corresponded to a true-positive fraction of 94.9% at a false positive fraction of 73.7%. The corresponding accuracy was 84.4%, the positive predictive value was 78.7\% and the negative predictive value was 93.3%.

Citation Download Citation

Wei Keat Lim and Meng Joo Er "Classification of mammographic masses using generalized dynamic fuzzy neural networks", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); https://doi.org/10.1117/12.481104

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